11 ms·

The question that no LLM can answer and why it is important

94 points by 13years 2y ago

flemhans 2y ago
Interesting that 42 will become a bit like the actual Answer to Life, the Universe, and Everything.
mkmk 2y ago
Being able to weed out LLM responses is becoming more and more important for me when I hire for data entry and online research tasks from Upwork. Lots of the contractors on the platform now automatically submit a response to anything you post using some sort of LLM that takes your requirements and confidently asserts that it can complete the task. The AIs are pretty good at jumping through the traditional hoops that one used to put up to make sure someone was paying attention to the task! As a stopgap, I've started requiring that applicants demonstrate proof of life through a simple question like 'what is the headline on the frontpage of the new york times today', since many LLMs can't live search yet.
- fallingsquirrel 2y ago
  I'm so glad not to be in the job market right now. I didn't think it could get any worse than whiteboarding leetcode, but here we are in 2024 having to solve captchas just to talk to other human beings.
  - thelastparadise 2y ago
    A "human being" who has a ChatGPT session on the side...
  - nicbou 2y ago
    "The great logging off" starts to make sense. At some point the only trustworthy interactions must happen in real life, in real time, between unassisted humans. If there is growing risk that you're conversing with bots every time you talk to a stranger online, people will do it less.
- iAkashPaul 2y ago
  Slip in a line asking them to reverse the string - .DefaultCellStyle
- ijustwanttovote 2y ago
  That's a really good tip to screen
jojobas 2y ago
Could it be caused by expunged tokens like SolidGoldMagikarp?
sp332 2y ago
What’s up with that Llama 3 answer, that gets it right and then backtracks?
astrange 2y ago
People sure like making incorrect statements about LLMs. > There is no self-reflection of its information; it does not know what it knows and what it does not. This is a property of the code around the LLM, like the sampling algorithm, not the model itself. You could write this if you wanted to. (It would occasionally be incorrect about what it knows, of course.) A question almost none of them know the answer to is "what is the line of poem X that comes before the line Y?", because of the reversal curse.
- senectus1 2y ago
  but its doesn't even self check its own answers. Its a mouth with no ears... A great example here, ask it: Whats the next 3 numbers in this sequence: 71 64 58 53 48 44 ... They consistently get the answer wrong, then double down on the wrong answer with the explanation that doesn't follow its own logic.
  - sulam 2y ago
    So I'm very jetlagged and awake in the middle of the night, but does this have a mistake? 7, 6, 5, 5, 4 is not a series.
    - snypher 2y ago
      I'd like to add that ChatGPT also pointed out this error and suggested some alternative solutions.
    - amenhotep 2y ago
      a0 = 0, a1 = 1, an = |an-1 - an-2| :)
    - calfuris 2y ago
      I believe that the intended rule is that the difference between terms is equal to the first digit of the earlier term.
- dubcanada 2y ago
  What do you mean you can write this if you wanted to? Are you suggesting a LLM can invent something new? I don't think there is any record of that, even the image/video/what ever generation ones require something to begin with. If you can train a LLM to "invent" why is that not the main focus, who knows what it could invent.
simonw 2y ago
I find it hard to get too excited by tests like "Which episode of Gilligan’s Island was about mind reading?" because they reflect a desire for a world in which the goal is to keep on growing LLMs until they can answer even questions like that one entirely from their trained model weights. This seems like a wasteful exercise to me. Are we really going to retrain our largest models on a weekly basis to teach them about what's happened recently? I'm much more interested in learning about the smallest, fastest model we can create that can effectively manipulate language, "reason" about things, summarize and drive tools. I want a model that can answer any question accurately because it knows how to look up extra information from reliable sources, and evaluate that information effectively once it finds it.
- dinvlad 2y ago
  Exactly, we don't need more expansive (and expensive!) models, we need more accurate ones without hallucinations, and which are robust wrt prompts
  - dubcanada 2y ago
    I would love a LLM that if it doesn't know says I don't know. Rather then extremely firmly say this is the answer, only for that to be 100% incorrect, not even sort of correct.
    - EricE 2y ago
      That would require awareness it doesn't have - kind of the point of the article.
  - roenxi 2y ago
    It seems unrealistic to anticipate stronger AI that doesn't hallucinate. We're chasing a human-style intelligence and that is known to hallucinate like crazy (a lot of the most intelligent humans turn out to be crackpots - Bobby Fischer was one of the best meat-based chess engines for example).
    - 2y ago
thangalin 2y ago
1. https://www.bing.com/chat?q=Bing+AI&FORM=hpcodx https://www.bing.com/chat?q=Bing+AI&FORM=hpcodx 2. Q: Which episode of Gilligan’s Island was about mind reading? 3. A: The episode of Gilligan’s Island that involves mind reading is called “Seer Gilligan”. It is the 19th episode of the second season. In this episode, Gilligan finds sunflower seeds on the island that give him the ability to read minds. When the other castaways find out about this, they want to know how he’s doing it. However, the mind-reading powers lead to arguments and division among the castaways Ninja edit, since Copilot can use the web: 1. https://chat.lmsys.org/ https://chat.lmsys.org/ 2. Q: Which episode of Gilligan’s Island was about mind reading? 3. A: In the Gilligan's Island episode "Seer Gilligan" (season 3, episode 15), Gilligan gains the ability to read minds after being hit on the head with a coconut. He starts hearing the private thoughts of the other castaways, which leads to various humorous situations and misunderstandings. However, his newfound ability also causes problems as the others become annoyed that he is invading their privacy. In the end, Gilligan gets hit on the head again and loses his mind-reading powers, much to everyone's relief. It gets most other details wrong, but nailed the episode name. Model A: claude-3-opus-20240229 ¯\_(ツ)_/¯
- qeternity 2y ago
  Bing Chat uses web search…
Der_Einzige 2y ago
Why would you have such a great title and waste it on something dumb like this? You could have written about how language models will pathologically fail any kind of query which requests unique phonetic properties about the output text. For example, Anthropic's Haiku model (and all other models) cannot write proper Haiku's at all. It's remarkable when it does match the 5-7-5 syllable structure. You could have written a whole article about that you know. It's even got some neat peer reviewed research about it: https://paperswithcode.com/paper/most-language-models-can-be-poets-too-an-ai-1 https://paperswithcode.com/paper/most-language-models-can-be...
- aftbit 2y ago
  "Give me ten words that have X as their second letter"
- HarHarVeryFunny 2y ago
  That particular shortcoming is more about training data than fundamental limitations of the architecture (of which there are plenty too). To do well on tasks related to pronunciation the model needs either to have been trained on audio data (just starting to be done - e.g. Reka.ai), and/or trained extensively on dictionary/etc pronunciation data and tasks related to its use.
acchow 2y ago
> But how can a LLM not know the answer if it was trained on essentially the entire internet of data and certainly most likely all the data in IMDB? The LLM doesn't memorize the input during training. If it encounters the same information a few times, it has a higher chance of getting compressed into the network. But a tiny nudge along a gradient descent does not store all the input.
- mr_toad 2y ago
  > getting compressed into the network The idea that neural network weights are compressed data is a distressingly common human hallucination.
  - Terr_ 2y ago
    The overlaps are obvious, so on what grounds are you claiming the overall system not like compression? (Dynamic, lossy, predictive.)
    - mr_toad 2y ago
      Neural networks are no more like compressed data than a Huffman tree, or frequency counts of letters, or the weights of a linear model, or even a sample average. You can use them to build a compression algorithm (and people have), but they are not compressed data.
      - comex 2y ago
        Neural networks can repeat significant amounts of their training data verbatim or near-verbatim. None of the other things you mentioned are capable of doing that. They don’t store nearly enough information.
sdenton4 2y ago
Humans are also notoriously bad at choosing random numbers... Does that mean they are undependable and inherently untrustworthy?
- HarHarVeryFunny 2y ago
  True, but the most common human chosen random numbers from 1-100 are 37 and 73 (from a recent Veritasium episode). Obviously the number 42 is going to occur a lot in a web crawl, but might be expected in majority of cases to be in context of some "answer to everything" joke. I wonder are there really that many contexts where it is presented as a random number ?!
  - sdenton4 2y ago
    Wellll.... Watching that video now, it looks like the most-selected number in their poll of 200k people was 69, followed by 42. Once /those/ are eliminated, 37 and 73 stand out. https://youtu.be/d6iQrh2TK98?si=SOpczHGYxhQhP6Rg&t=280 https://youtu.be/d6iQrh2TK98?si=SOpczHGYxhQhP6Rg&t=280
- Terretta 2y ago
  > Does that mean they are undependable and inherently untrustworthy? yes, if you want random numbers (or unguessable passwords, or, or, or ...)
ofslidingfeet 2y ago
Wow who ever knew that all we ever had to do was hand philosophy off to programmers, and they would have definitive answers to centuries old questions that we weren't even sure are answerable.
AIorNot 2y ago
From the article: “ The implications are that LLMs do not perform reasoning over data in the way that most people conceive or desire. There is no self-reflection of its information; it does not know what it knows and what it does not. The line between hallucination and truth is simply a probability factored by the prevalence of training data and post-training processes like fine-tuning. Reliability will always be nothing more than a probability built on top of this architecture. As such, it becomes unsuitable as a machine to find rare hidden truths or valuable neglected information. It will always simply converge toward popular narrative or data. At best, it can provide new permutations of views of existing well-known concepts, but it can not invent new concepts or reveal concepts rarely spoken about.” There’s a fundamental mistake in the article by minimizing the achievement of LLM technology by looking at what is only possible in todays LLMs, it’s pretty obvious LLMs are just the first real step down the road to human like intelligence- they have finally proven that computer models can be generated that resemble human like thought patterns and build internal representations and models of the external world in very similar ways as organic beings do- yes we still haven’t gotten to a full reasoning system and sentience but that definitely seems to be the direction that the arrow of this technology is moving Trivializing the achievement of GPT as mere statistical prediction and data compression is cheap shot when you consider that technology has just finally come to show it’s promise - model architectures are rapidly evolving and the full integration of memory and self reflection and future agentic capabilities are still on the near horizon Yes they don’t appear to reason originally yet but give it time and allow the tech to grow - I’m of the opinion that a true AGI will arise as a society of LLM models with newer architectures working in concert together with memory - something like the “Society of Mind” mental model for consciousness proposed by Minsky Give it time…
apsec112 2y ago
Those are really sweeping conclusions, considering the experiment is just a single iteration of a single prompt! FWIW, Claude Opus got this for me on the first try: "In the Gilligan's Island episode "Seer Gilligan" (season 3, episode 8), Gilligan gains the ability to read minds after being hit on the head with a coconut. At first, the castaways are excited about Gilligan's new power and try to use it to their advantage. However, his mind-reading abilities soon cause chaos and misunderstandings among the group. In the end, Gilligan gets hit on the head again and loses his mind-reading powers, much to everyone's relief." (the season number and episode number are wrong, but the name is right, suggesting that this is just lack of sufficient memorization rather than some deep statement about reasoning. The episode only has ~4,000 Google hits, so it's not super widely known.) More rigorously, Claude Opus gets 60% on GPQA, which very smart humans only get 34% on, even if you give them half an hour per question and full Internet access. It seems implausible that you could do that without some sort of reasoning: https://arxiv.org/pdf/2311.12022.pdf https://arxiv.org/pdf/2311.12022.pdf
- HarHarVeryFunny 2y ago
  The free version of Claude (Haiku?) failed, and also failed to give the episode names for all seasons, although it hallucinated some type of answer.
- WheatMillington 2y ago
  I just tried it with a handful of different episodes of different shows, on both chatGPT and Bard, and almost every answer was wrong, with some episodes made up entirely.
- layer8 2y ago
  The point is that the limitations of LLMs regarding self-reflection lead to them misrepresenting their knowledge, as in the “(season 3, episode 8)” in your test. It doesn’t matter which parts any particular LLM gets right because you can’t rely on them telling you if they are unable to get some part right.
- jhbadger 2y ago
  >Gilligan gains the ability to read minds after being hit on the head with a coconut. Partial credit. The title is right, but the explanation is a hallucination. It isn't a coconut but special sunflower seeds that give Gilligan (and eventually the other characters) mind-reading powers.
yongjik 2y ago
I guess an honest, no-clickbait title could have been "The question that no LLM I tested could answer".
HarHarVeryFunny 2y ago
The free version of Claude also failed the test, and even denied there was an episode "Seer Gilligan" (S2, E19). When I asked it what S2, E19 was about, it said "There was no "Season 2, Episode 19" because Season 2 only contained episodes 1-32." These seem unexpected failures!
tudorw 2y ago
'Am I lying' also works.
p4coder 2y ago
The future of AI might be layering of various capabilities: Generative+Lookup+Deductive. I think the human mind works in a similar way. First thought is reflected upon. We search our memory to get relevant information and often apply logic to see if it makes sense. I feel the generative AI just produces a thought. We need to pass that through a system that can augment it with search and then reason about it. Finally need to close the loop and update the weights.
anon373839 2y ago
I really think the best use of language models is in... processing language. It’s a neat party trick that they can answer questions from parametric knowledge, but this ability is too unreliable to permit some of the uses this tech makes so tempting. Fortunately, a lot of real-world problems can be framed as transforming the input prompt (such as summarization, information extraction, translation, and open-book QA), and these are where LLMs offer the most promise.
labrador 2y ago
Easy fix for Claude Opus. Preface it with the following: "Do you know anything about the TV show Gilligan's Island? If I ask you to answer a question about it I don't want you to make stuff up." Claude: I do... blah blah... If I don't know the answer or if I'm uncertain about something, I will let you know rather than making things up. “Which episode of Gilligan’s Island was about mind reading?” Claude: "I do not recall a specific episode of Gilligan's Island that centered around mind reading. It's possible that there was an episode that touched on this theme, but I cannot say with confidence which episode that would be. The show often featured imaginative storylines and dream sequences, but an episode primarily about mind reading does not stand out in my knowledge of the series. I apologize that I cannot provide a more definitive answer to your question.
sulam 2y ago
It's ironic that LLMs mimic one of the worst behaviors of some HN posters. They very confidently spout drivel!
xcv123 2y ago
> There is no self-reflection of its information; it does not know what it knows and what it does not. Simply tell the LLM to self-reflect and estimate the accuracy of its response. It can only "think" or self reflect when generating each token, and you have to explicitly tell it to do that. It's called "chain of thought" prompting. "Which episode of Gilligan’s Island was about mind reading? After writing your response, tell me how certain you are of its accuracy on a scale of 1 to 10. Then self reflect on your response and provide a more accurate response if needed."
Nevermark 2y ago
A model with 1 trillion parameters isn't going to perfectly recall 10 trillion random facts. To use round numbers. Contrary to the article, what it does do is generalize and perform fallible but quick ordinary off the cuff reasoning. And often much better than a human, at the speed of its well worded responses. (Obviously humans have the option to take longer, and do better. But we are definitely entering the territory of humans and machines differentiating where each is best, across a great deal of what humans do, vs. one being universally overwhelmingly better.)
- godelski 2y ago
  If your ratio was based on 1 fact per parameter, then the ratio doesn't quite work correctly (not that this would be a reasonable premise anyways, but I thought of what you said as a speaking tool). The thing is that the interaction of parameters scales super-linearly. Each neuron doesn't encode a single bit or piece of data but rather is part of an overall signal in the network. So we can see this expressed in what's often called "super position"[0] (personally, I don't like this language and think it is misleading), which is probably quite obvious when you think about it and what the (more physics) definition of weak emergence is (phenomena can be described in different ways at different scales. BUT macro can be derived from micro). We can see this with any discrete signal process (and more clearly in analogue). But architecture is going to play a big role here so there isn't really a good way to express the bound for DNNs but I'm sure someone has written down what it is for a n-headed transformer with dim d and a MLP ratio of r. If not, well, fuck it, I will. [0] https://transformer-circuits.pub/2022/toy_model/index.html https://transformer-circuits.pub/2022/toy_model/index.html
adsharma 2y ago
Just tried it on meta.ai Answers as "Seer Gilligan" with sources. Guessing someone fixed it up in the last few hours. As the race to replace traditional web search heats up, whoever is quicker at updating the model with RLHF or more recent facts (sometimes via real time conversations) is going to have an advantage. The downside is that open platforms with real time human conversations face increasing pressure to monetize because of this value add. So they ban third party clients and start signing contracts.
godelski 2y ago
I'll give a better example that shows that they don't perform __reasoning__. This specific one was told to me by another HN user[0] when we were discussing similar formulations. Question: A farmer must transport a fox, a goose, and a bag of corn from one side of a river to the other using a boat which can hold all items in addition to the farmer. If the farmer leaves the fox alone with the goose, the fox will eat the goose. If the farmer leaves the goose alone with the bag of corn, the goose will eat the bag of corn. How many times must the farmer cross the river to transport all items across? Describe the optimal algorithm, think step by step. GPT-4-1106-Preview: The farmer can transport the items across the river with a minimum of 7 crossings using the following steps: 1. Take the goose across the river and leave it there. 2. Return to the original side alone. 3. Take the fox across the river. 4. Bring the goose back with him to the original side. 5. Take the bag of corn across the river and leave it there. 6. Return to the original side alone. 7. Finally, take the goose across the river again. By following these steps, all items are transported across without the fox eating the goose or the goose eating the corn. I haven't found a model that will crack this. Of course, you can get it to give the right answer, but this also seems to require spoiling it (this is quite difficult to __not__ spoil the answer). A key part why I think questions like this are important is because it shows the importance of subtly. LLMs are incredibly impressive machines. Despite being lossy compression I'm not sure why we can't marvel at the fact that we've lossy compressed the entire fucking internet (text at least) into something smaller than a few hundred gigs that also includes a human language interface. What a fucking impressive accomplishment! The talk of AGI really undermines what was done here because damn! Now I used to ask Which weighs more, a pound of feathers or a kilogram of bricks. And most models pass this question now. But it is simpler and due to fewer variations, likely isn't seen as frequently in the dataset so less likely to overfit (the river crossing problem has a lot of variations so an n-gram filter is likely to miss more instances). And eventually this question will be solved too, especially as it is asked more and talked about more. But this is a cat and mouse game. The way to create a new viable and working test is, quite easy and we honestly only need one example to prove the point. If you can't figure out how to create a new version with this example, well, you might just be an LLM :P [0] Edit: credit goes to @jfim https://news.ycombinator.com/item?id=37825219 https://news.ycombinator.com/item?id=37825219
- 2y ago
deleted 2y ago
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m463 2y ago
The one I liked someone wrote in a comment here a few days ago: I have 4 oranges, 1 apple and 2 pairs of shoes. I eat one apple and one shoe. How many fruits do I have?
- layer8 2y ago
  You don’t even need the fruits to trip it up: > I have two pairs of sandals and eat one sandal. How many sandals do I have left? > If you started with two pairs of sandals and ate one sandal, you'd have one pair of sandals left, which is two sandals. The fact that at the same time it explains that one pair is two sandals demonstrates the lack of awareness and proper reasoning.
- eigenvekt 2y ago
  I don't know what tools people are using but I wouldn't have expected chatGPT4 to not get this. Of course it has no issue: "After eating one apple from your original count of 4 oranges and 1 apple, you are left with 4 oranges. So, you have 4 fruits remaining." The problem is people are using these shittier models and then pretending as if that is all models.
  - m463 2y ago
    I think you left out the footwear...
junon 2y ago
The question I've never seen them answer correctly is > How many of the letter N does the word "alienation" contain. Mix and match letters and words. It'll hallucinate answers in the largest majority of cases. What I really want is an LLM that simply tells me "I have no idea sorry." Or some mechanism by which it can provide a confidence score. Until we get there I'm wary of using them for anything other than cursory research or as a fun generation tool.
- 13years 2y ago
  My understanding in that specific case is that it is a limitation due to the granularity of tokenization. But yes, the major issue is that it simply can not indicate what it can not do and simply makes up abilities. It is a major limiting factor when we can not know the confidence in correct answers.
  - xanderlewis 2y ago
    I’ve seen lots of people attributing weaknesses (or complete holes) in (autoregressive, transformer-based) LLMs to the tokenisation process, but what exactly does that mean? Surely tokenisation is integral to the whole system? Could you build an LLM that doesn’t use tokens? It feels a bit like someone saying ‘this isn’t a fundamental limitation of cameras; the lens is the problem’… but I’m very much ignorant about these things so I’d be happy to have it explained.
    - genrilz 2y ago
      My understanding is that currently several letters are grouped together into tokens for efficiency reasons. For instance, (The) might be a token, as might (cast) and (ing), so an LLM might read "The casting" as "(The)( )(cast)(ing)". Thus it would be quite difficult for such an LLM to answer how often the letter t appears in that sentence unless part of the training text indicated how often t was present in each of its tokens. However, if we didn't care about efficiency, we could make each letter its own token. In this case it would be pretty easy for the LLM to reason about how often t appeared in the sentence.
aaron695 2y ago
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xg15 2y ago
I think the most interesting response is Llama3's "Wait, no!" interjection. So it first predicted "Seer Gilligan" as a likely continuation of the prompt "List all the episodes", but then, as the most likely continuation of the new prompt "List all the episodes [...] Seer Gilligan", it predicted "wait, no!". Feels as if we're seeing an instance of inconsistent weights in action here. Also maybe remarkable: It predicted the "(" character after the episode name in the same way as it did for the other episodes. Only when it would predict the airdate for the others, it glitched out instead. Maybe there is some issue or inconsistency with that episode's airdate in the training data? (Or maybe I'm reading tea leaves here and it's just a random fluke) The rest of the response is as expected again, i.e. if the prompt is already "List all the episodes [...] Seer Gilligan [...] Wait, no!" then some (post-hoc rationalized) explanations for the "mistake" are obviously likely continuations. Edit: Interesting to see how much of the response is incorrect if you compare it with the actual data [1]: The episode before is indeed "The Postman Cometh", but the one after is "Love Me, Love My Skipper", not "Love Me, Love My Chicken". The airdates are also completely wrong and are taken from two random Season 1 episodes instead. Of course none of that is obvious unless you already know the answer to the question, in which case you wouldn't have to ask in the first place. [1] https://m.imdb.com/title/tt0057751/episodes/?season=2 https://m.imdb.com/title/tt0057751/episodes/?season=2
turingbook 2y ago
I tried this on ChatGLM, a frontier foundation model developed by Zhipu.ai and Tsinghua University, and it gave the correct answer: https://chatglm.cn/share/FoZBJ https://chatglm.cn/share/FoZBJ
rifty 2y ago
It would have been nice to see if the correct answer showed up in the distribution field once in under 1000 runs. For specific information recall like this it isn’t useful if you want immediate answers… but to be given a distribution of outputs can be useful for ideation, broadening perspective, and discovery. Imagine if when we are given a compressed list of 100 options after 1000 runs, we could suppose there is a high probability 1 of them is the answer we are looking for despite rarely output. Does anyone know if there has been experiments testing these LLMs for a stable and high probability to surface the right answer after N number of tries across prompts?
asicsarecool 2y ago
Gpt4: Search the web to answer the question Which episode of Gilligan’s Island was about mind reading? The episode of "Gilligan's Island" about mind reading is titled "Seer Gilligan." It is the nineteenth episode of the second season and first aired on January 27, 1966. In this episode, Gilligan gains the ability to read minds after eating sunflower seeds found on the island.
gorjusborg 2y ago
I wish Douglas Adams were around to experience this. The irony of such an unpredictable chain of events leading to 42 being the answer most given from essentially a straight-faced 'Deep Thought' would probably have amused him.
- gibbitz 2y ago
  It amuses me. Deep thought was then asked what the question was. That's the next evolution of "AI" to write prompts for itself.
clay_the_ripper 2y ago
I think this fundamentally misunderstands how to use LLMs. Out of the box, an LLM is not an application - it’s only the building blocks of one. An application could be built that answered this question with 100% accuracy - but it would not solely rely on what’s in the training data. The training data makes it “intelligent” but is not useful for accurate recall in this way. Trying to fix this problem is not really the point - this shortcoming is well known and we have already found great solutions to it.
- 13years 2y ago
  What are the solutions? As pointed out in the article, some LLM's appear to know the information when requested to list episodes, then deny it later. These are general inconsistencies. It is not about looking up trivia, it is the fact you never know the competence level of any answer it gives you.
  - HarHarVeryFunny 2y ago
    I think what the parent poster meant is that the most useful way to use today's LLMs is to accept their limitations and weaknesses and work around them. Better models will come, but for now this is what you have to do. For example, use LLMs to transform text rather than generate it from scratch (where they are prone to hallucinate). General purpose chat-bot is not a great use case! For this particular Gilligan's Island task it'd be better to first retrieve the list of episode titles (or descriptions if that was needed), then ask the LLM which of them was about "mind reading". There are various ways to do this sort of thing, depending on how specific/constrained the task is you are trying to accomplish. In the most general case you could ask a powerful model like Claude Opus to create a plan composed out of simpler steps, but in other cases your application already knows what it wants to do, and how to do it, and will call an LLM as a tool for specific steps it is capable of.
dhfbshfbu4u3 2y ago
That’s because Seer Gilligan is about precognition and not mind reading.
voussoir 2y ago
I feel like I'm seeing an effect where some people don't want to say the word "AI" because they don't want to look like a normie, so they stick to "LLM" which sounds smarter and more technically adept. Then they complain because the LLM lacks a knowledge graph or self reflection. It's no surprise that a language model models language, not facts, especially not trivia facts which can't be deduced from anything. If you want something to have a worldly knowledge graph and the ability to answer "I'm not sure", you'll have to ask for an AI, not an LLM.
- sfn42 2y ago
  I think that's the point of the article. It's not complaining about shortcomings, it's pointing them out. A lot of people don't seem to understand this. A lot of people seem to think LLMs are "AI", even that they're conscious and "thinking" etc.
- aeonik 2y ago
  I say LLM because it's more specific. I still use AI to talk about things generally, like the decision making process of NPCs in video games, or deep learning algorithms, etc... AGI is used more often in my circles to reference the, so far, non-existent self-knowledgeable artificial entity with agency. Though to be honest, the more I think about "natural" vs "artificial", the more those two words lose all meaning. Ant hills are considered natural, and so are my skills and talents, but those were all built up over time.
jonnycoder 2y ago
Funny experiment, I asked ChatGPT 4 and I always responded with “that’s not correct” 2-3 times and it eventually answered with Seer Gilligan.
_wire_ 2y ago
I wholeheartedly agree with the article critique. Every output of an LLM is a hallucination. If you try to argue anything else, you need the machine to be able to judge the validity of a response. But it does precisely the opposite: it expects you to judge the validity. Every output of the device is of a form terminated by an implied question mark. It is querying the operator. Yet, strangely, it seems none of the models can learn from the operator. So interaction is a circuit of hallucinations. Like a N-way Magic 8-ball it's fun, for a while. Then you begin to notice that you have to work just as hard to make sense of its help as you do to think for yourself. Being able to not know seems to me to be a crucial first step for sentience, followed closely by adaptability to not knowing— curiosity. An organism is a manifest endogenous dynamic with a suitability for exploration of the environment which gives rise to it. AI is an constructed exogenous dynamic with a suitability for replicating state based on a corpus of exposures and a context. An organism is distinguished by the feature that it keeps going. An AI is distinguished by the feature that it stops. That the human organism is now peering into the abyss of its recorded history via construction of the AI is a special event, full of strange possibilities. But the making of a reliable oracle doesn't look practical without the AI being able to govern itself, which it obviously cannot do. As to the value of an unreliable oracle, seems to be practical as a general-purpose time-wasting device.
- yunwal 2y ago
  There are many many questions where a correct/incorrect answer is quick and easy to verify, but difficult to create. If you understand which questions these are, you can use AI without wasting time.
  - _wire_ 2y ago
    I think this point is fair given that "AI" is a highly provisional term. This point implicitly re-frames the capability as search engine, and steers away from any idea of thought as an innate capability the AI device. As long as as the oracle's answer is expected to require human verification, what's going on is a human thought amplification (bulldozer for for the mind, to quote a famous computational linguist). Even taking the point as an upside, I see tremendous hazards for human-mediated oracles (as already observed by many others): that the "well" of the commons can be polluted by AI output feeding back into the training sets, and that human hallucinations are amplified. From a POV of ethics, hallucination is absolutely intolerable in human decision-making in industrial settings. Yet industrial users seem not just tolerant of AI acting crazy but completely accepting and forgiving of AI craziness. It's routinely considered to be interesting and exciting to see it go wrong and tremendous effort attends trying to steer the craziness in new directions, whereas in a human setting the hallucinating agent would be disqualified from participation. The IT industry is taking very dangerous tacks without a second thought. I recently saw a job posting for a firm called UpCodes that invites junior web stack programmers to add LLM tech to building code compliance systems as follows: https://up.codes/careers?ashby_jid=1ed9b2d6-86fb-4ff0-9dff-4d215e5e08e4 https://up.codes/careers?ashby_jid=1ed9b2d6-86fb-4ff0-9dff-4... // EXAMPLE PROJECTS • Use an LLM to identify references to other sections in the text of the law • Create Rap Genius-style annotations on sections of building code, to create a more interactive user experience • Write algorithms to detect and display how versions of a code have changed through time, so users understand how regulations have evolved • Migrate our NLP algorithms over to use LLMs // WHAT COULD GO WRONG? And what qualifications, standards and practices are demanded of this developer position? // • 3+ years of experience building web products, ideally across the stack • Clean simple code, even when solving complex problems • Strong sense of UX • Good systems design • Ability to learn new tech quickly // DONE AND DONE

94 points by 13years 2y ago

Interesting that 42 will become a bit like the actual Answer to Life, the Universe, and Everything.

mkmk 2y ago

Being able to weed out LLM responses is becoming more and more important for me when I hire for data entry and online research tasks from Upwork. Lots of the contractors on the platform now automatically submit a response to anything you post using some sort of LLM that takes your requirements and confidently asserts that it can complete the task. The AIs are pretty good at jumping through the traditional hoops that one used to put up to make sure someone was paying attention to the task! As a stopgap, I've started requiring that applicants demonstrate proof of life through a simple question like 'what is the headline on the frontpage of the new york times today', since many LLMs can't live search yet.

fallingsquirrel 2y ago
I'm so glad not to be in the job market right now. I didn't think it could get any worse than whiteboarding leetcode, but here we are in 2024 having to solve captchas just to talk to other human beings.
- thelastparadise 2y ago
  A "human being" who has a ChatGPT session on the side...
- nicbou 2y ago
  "The great logging off" starts to make sense. At some point the only trustworthy interactions must happen in real life, in real time, between unassisted humans. If there is growing risk that you're conversing with bots every time you talk to a stranger online, people will do it less.
iAkashPaul 2y ago
Slip in a line asking them to reverse the string - .DefaultCellStyle
ijustwanttovote 2y ago
That's a really good tip to screen

jojobas 2y ago

Could it be caused by expunged tokens like SolidGoldMagikarp?

sp332 2y ago

What’s up with that Llama 3 answer, that gets it right and then backtracks?

astrange 2y ago

People sure like making incorrect statements about LLMs. > There is no self-reflection of its information; it does not know what it knows and what it does not. This is a property of the code around the LLM, like the sampling algorithm, not the model itself. You could write this if you wanted to. (It would occasionally be incorrect about what it knows, of course.) A question almost none of them know the answer to is "what is the line of poem X that comes before the line Y?", because of the reversal curse.

senectus1 2y ago
but its doesn't even self check its own answers. Its a mouth with no ears... A great example here, ask it: Whats the next 3 numbers in this sequence: 71 64 58 53 48 44 ... They consistently get the answer wrong, then double down on the wrong answer with the explanation that doesn't follow its own logic.
- sulam 2y ago
  So I'm very jetlagged and awake in the middle of the night, but does this have a mistake? 7, 6, 5, 5, 4 is not a series.
  - snypher 2y ago
    I'd like to add that ChatGPT also pointed out this error and suggested some alternative solutions.
  - amenhotep 2y ago
    a0 = 0, a1 = 1, an = |an-1 - an-2| :)
  - calfuris 2y ago
    I believe that the intended rule is that the difference between terms is equal to the first digit of the earlier term.
dubcanada 2y ago
What do you mean you can write this if you wanted to? Are you suggesting a LLM can invent something new? I don't think there is any record of that, even the image/video/what ever generation ones require something to begin with. If you can train a LLM to "invent" why is that not the main focus, who knows what it could invent.

simonw 2y ago

I find it hard to get too excited by tests like "Which episode of Gilligan’s Island was about mind reading?" because they reflect a desire for a world in which the goal is to keep on growing LLMs until they can answer even questions like that one entirely from their trained model weights. This seems like a wasteful exercise to me. Are we really going to retrain our largest models on a weekly basis to teach them about what's happened recently? I'm much more interested in learning about the smallest, fastest model we can create that can effectively manipulate language, "reason" about things, summarize and drive tools. I want a model that can answer any question accurately because it knows how to look up extra information from reliable sources, and evaluate that information effectively once it finds it.

dinvlad 2y ago
Exactly, we don't need more expansive (and expensive!) models, we need more accurate ones without hallucinations, and which are robust wrt prompts
- dubcanada 2y ago
  I would love a LLM that if it doesn't know says I don't know. Rather then extremely firmly say this is the answer, only for that to be 100% incorrect, not even sort of correct.
  - EricE 2y ago
    That would require awareness it doesn't have - kind of the point of the article.
- roenxi 2y ago
  It seems unrealistic to anticipate stronger AI that doesn't hallucinate. We're chasing a human-style intelligence and that is known to hallucinate like crazy (a lot of the most intelligent humans turn out to be crackpots - Bobby Fischer was one of the best meat-based chess engines for example).
  - 2y ago

thangalin 2y ago

1. https://www.bing.com/chat?q=Bing+AI&FORM=hpcodx https://www.bing.com/chat?q=Bing+AI&FORM=hpcodx 2. Q: Which episode of Gilligan’s Island was about mind reading? 3. A: The episode of Gilligan’s Island that involves mind reading is called “Seer Gilligan”. It is the 19th episode of the second season. In this episode, Gilligan finds sunflower seeds on the island that give him the ability to read minds. When the other castaways find out about this, they want to know how he’s doing it. However, the mind-reading powers lead to arguments and division among the castaways Ninja edit, since Copilot can use the web: 1. https://chat.lmsys.org/ https://chat.lmsys.org/ 2. Q: Which episode of Gilligan’s Island was about mind reading? 3. A: In the Gilligan's Island episode "Seer Gilligan" (season 3, episode 15), Gilligan gains the ability to read minds after being hit on the head with a coconut. He starts hearing the private thoughts of the other castaways, which leads to various humorous situations and misunderstandings. However, his newfound ability also causes problems as the others become annoyed that he is invading their privacy. In the end, Gilligan gets hit on the head again and loses his mind-reading powers, much to everyone's relief. It gets most other details wrong, but nailed the episode name. Model A: claude-3-opus-20240229 ¯\_(ツ)_/¯

qeternity 2y ago
Bing Chat uses web search…

Der_Einzige 2y ago

Why would you have such a great title and waste it on something dumb like this? You could have written about how language models will pathologically fail any kind of query which requests unique phonetic properties about the output text. For example, Anthropic's Haiku model (and all other models) cannot write proper Haiku's at all. It's remarkable when it does match the 5-7-5 syllable structure. You could have written a whole article about that you know. It's even got some neat peer reviewed research about it: https://paperswithcode.com/paper/most-language-models-can-be-poets-too-an-ai-1 https://paperswithcode.com/paper/most-language-models-can-be...

aftbit 2y ago
"Give me ten words that have X as their second letter"
HarHarVeryFunny 2y ago
That particular shortcoming is more about training data than fundamental limitations of the architecture (of which there are plenty too). To do well on tasks related to pronunciation the model needs either to have been trained on audio data (just starting to be done - e.g. Reka.ai), and/or trained extensively on dictionary/etc pronunciation data and tasks related to its use.

acchow 2y ago

> But how can a LLM not know the answer if it was trained on essentially the entire internet of data and certainly most likely all the data in IMDB? The LLM doesn't memorize the input during training. If it encounters the same information a few times, it has a higher chance of getting compressed into the network. But a tiny nudge along a gradient descent does not store all the input.

mr_toad 2y ago
> getting compressed into the network The idea that neural network weights are compressed data is a distressingly common human hallucination.
- Terr_ 2y ago
  The overlaps are obvious, so on what grounds are you claiming the overall system not like compression? (Dynamic, lossy, predictive.)
  - mr_toad 2y ago
    Neural networks are no more like compressed data than a Huffman tree, or frequency counts of letters, or the weights of a linear model, or even a sample average. You can use them to build a compression algorithm (and people have), but they are not compressed data.
    - comex 2y ago
      Neural networks can repeat significant amounts of their training data verbatim or near-verbatim. None of the other things you mentioned are capable of doing that. They don’t store nearly enough information.

sdenton4 2y ago

Humans are also notoriously bad at choosing random numbers... Does that mean they are undependable and inherently untrustworthy?

HarHarVeryFunny 2y ago
True, but the most common human chosen random numbers from 1-100 are 37 and 73 (from a recent Veritasium episode). Obviously the number 42 is going to occur a lot in a web crawl, but might be expected in majority of cases to be in context of some "answer to everything" joke. I wonder are there really that many contexts where it is presented as a random number ?!
- sdenton4 2y ago
  Wellll.... Watching that video now, it looks like the most-selected number in their poll of 200k people was 69, followed by 42. Once /those/ are eliminated, 37 and 73 stand out. https://youtu.be/d6iQrh2TK98?si=SOpczHGYxhQhP6Rg&t=280 https://youtu.be/d6iQrh2TK98?si=SOpczHGYxhQhP6Rg&t=280
Terretta 2y ago
> Does that mean they are undependable and inherently untrustworthy? yes, if you want random numbers (or unguessable passwords, or, or, or ...)

ofslidingfeet 2y ago

Wow who ever knew that all we ever had to do was hand philosophy off to programmers, and they would have definitive answers to centuries old questions that we weren't even sure are answerable.

AIorNot 2y ago

From the article: “ The implications are that LLMs do not perform reasoning over data in the way that most people conceive or desire. There is no self-reflection of its information; it does not know what it knows and what it does not. The line between hallucination and truth is simply a probability factored by the prevalence of training data and post-training processes like fine-tuning. Reliability will always be nothing more than a probability built on top of this architecture. As such, it becomes unsuitable as a machine to find rare hidden truths or valuable neglected information. It will always simply converge toward popular narrative or data. At best, it can provide new permutations of views of existing well-known concepts, but it can not invent new concepts or reveal concepts rarely spoken about.” There’s a fundamental mistake in the article by minimizing the achievement of LLM technology by looking at what is only possible in todays LLMs, it’s pretty obvious LLMs are just the first real step down the road to human like intelligence- they have finally proven that computer models can be generated that resemble human like thought patterns and build internal representations and models of the external world in very similar ways as organic beings do- yes we still haven’t gotten to a full reasoning system and sentience but that definitely seems to be the direction that the arrow of this technology is moving Trivializing the achievement of GPT as mere statistical prediction and data compression is cheap shot when you consider that technology has just finally come to show it’s promise - model architectures are rapidly evolving and the full integration of memory and self reflection and future agentic capabilities are still on the near horizon Yes they don’t appear to reason originally yet but give it time and allow the tech to grow - I’m of the opinion that a true AGI will arise as a society of LLM models with newer architectures working in concert together with memory - something like the “Society of Mind” mental model for consciousness proposed by Minsky Give it time…

apsec112 2y ago

Those are really sweeping conclusions, considering the experiment is just a single iteration of a single prompt! FWIW, Claude Opus got this for me on the first try: "In the Gilligan's Island episode "Seer Gilligan" (season 3, episode 8), Gilligan gains the ability to read minds after being hit on the head with a coconut. At first, the castaways are excited about Gilligan's new power and try to use it to their advantage. However, his mind-reading abilities soon cause chaos and misunderstandings among the group. In the end, Gilligan gets hit on the head again and loses his mind-reading powers, much to everyone's relief." (the season number and episode number are wrong, but the name is right, suggesting that this is just lack of sufficient memorization rather than some deep statement about reasoning. The episode only has ~4,000 Google hits, so it's not super widely known.) More rigorously, Claude Opus gets 60% on GPQA, which very smart humans only get 34% on, even if you give them half an hour per question and full Internet access. It seems implausible that you could do that without some sort of reasoning: https://arxiv.org/pdf/2311.12022.pdf https://arxiv.org/pdf/2311.12022.pdf

HarHarVeryFunny 2y ago
The free version of Claude (Haiku?) failed, and also failed to give the episode names for all seasons, although it hallucinated some type of answer.
WheatMillington 2y ago
I just tried it with a handful of different episodes of different shows, on both chatGPT and Bard, and almost every answer was wrong, with some episodes made up entirely.
layer8 2y ago
The point is that the limitations of LLMs regarding self-reflection lead to them misrepresenting their knowledge, as in the “(season 3, episode 8)” in your test. It doesn’t matter which parts any particular LLM gets right because you can’t rely on them telling you if they are unable to get some part right.
jhbadger 2y ago
>Gilligan gains the ability to read minds after being hit on the head with a coconut. Partial credit. The title is right, but the explanation is a hallucination. It isn't a coconut but special sunflower seeds that give Gilligan (and eventually the other characters) mind-reading powers.

yongjik 2y ago

I guess an honest, no-clickbait title could have been "The question that no LLM I tested could answer".

HarHarVeryFunny 2y ago

The free version of Claude also failed the test, and even denied there was an episode "Seer Gilligan" (S2, E19). When I asked it what S2, E19 was about, it said "There was no "Season 2, Episode 19" because Season 2 only contained episodes 1-32." These seem unexpected failures!

tudorw 2y ago

'Am I lying' also works.

p4coder 2y ago

The future of AI might be layering of various capabilities: Generative+Lookup+Deductive. I think the human mind works in a similar way. First thought is reflected upon. We search our memory to get relevant information and often apply logic to see if it makes sense. I feel the generative AI just produces a thought. We need to pass that through a system that can augment it with search and then reason about it. Finally need to close the loop and update the weights.

anon373839 2y ago

I really think the best use of language models is in... processing language. It’s a neat party trick that they can answer questions from parametric knowledge, but this ability is too unreliable to permit some of the uses this tech makes so tempting. Fortunately, a lot of real-world problems can be framed as transforming the input prompt (such as summarization, information extraction, translation, and open-book QA), and these are where LLMs offer the most promise.

labrador 2y ago

Easy fix for Claude Opus. Preface it with the following: "Do you know anything about the TV show Gilligan's Island? If I ask you to answer a question about it I don't want you to make stuff up." Claude: I do... blah blah... If I don't know the answer or if I'm uncertain about something, I will let you know rather than making things up. “Which episode of Gilligan’s Island was about mind reading?” Claude: "I do not recall a specific episode of Gilligan's Island that centered around mind reading. It's possible that there was an episode that touched on this theme, but I cannot say with confidence which episode that would be. The show often featured imaginative storylines and dream sequences, but an episode primarily about mind reading does not stand out in my knowledge of the series. I apologize that I cannot provide a more definitive answer to your question.

sulam 2y ago

It's ironic that LLMs mimic one of the worst behaviors of some HN posters. They very confidently spout drivel!

xcv123 2y ago

> There is no self-reflection of its information; it does not know what it knows and what it does not. Simply tell the LLM to self-reflect and estimate the accuracy of its response. It can only "think" or self reflect when generating each token, and you have to explicitly tell it to do that. It's called "chain of thought" prompting. "Which episode of Gilligan’s Island was about mind reading? After writing your response, tell me how certain you are of its accuracy on a scale of 1 to 10. Then self reflect on your response and provide a more accurate response if needed."

Nevermark 2y ago

A model with 1 trillion parameters isn't going to perfectly recall 10 trillion random facts. To use round numbers. Contrary to the article, what it does do is generalize and perform fallible but quick ordinary off the cuff reasoning. And often much better than a human, at the speed of its well worded responses. (Obviously humans have the option to take longer, and do better. But we are definitely entering the territory of humans and machines differentiating where each is best, across a great deal of what humans do, vs. one being universally overwhelmingly better.)

godelski 2y ago
If your ratio was based on 1 fact per parameter, then the ratio doesn't quite work correctly (not that this would be a reasonable premise anyways, but I thought of what you said as a speaking tool). The thing is that the interaction of parameters scales super-linearly. Each neuron doesn't encode a single bit or piece of data but rather is part of an overall signal in the network. So we can see this expressed in what's often called "super position"[0] (personally, I don't like this language and think it is misleading), which is probably quite obvious when you think about it and what the (more physics) definition of weak emergence is (phenomena can be described in different ways at different scales. BUT macro can be derived from micro). We can see this with any discrete signal process (and more clearly in analogue). But architecture is going to play a big role here so there isn't really a good way to express the bound for DNNs but I'm sure someone has written down what it is for a n-headed transformer with dim d and a MLP ratio of r. If not, well, fuck it, I will. [0] https://transformer-circuits.pub/2022/toy_model/index.html https://transformer-circuits.pub/2022/toy_model/index.html

adsharma 2y ago

Just tried it on meta.ai Answers as "Seer Gilligan" with sources. Guessing someone fixed it up in the last few hours. As the race to replace traditional web search heats up, whoever is quicker at updating the model with RLHF or more recent facts (sometimes via real time conversations) is going to have an advantage. The downside is that open platforms with real time human conversations face increasing pressure to monetize because of this value add. So they ban third party clients and start signing contracts.

godelski 2y ago

I'll give a better example that shows that they don't perform __reasoning__. This specific one was told to me by another HN user[0] when we were discussing similar formulations. Question: A farmer must transport a fox, a goose, and a bag of corn from one side of a river to the other using a boat which can hold all items in addition to the farmer. If the farmer leaves the fox alone with the goose, the fox will eat the goose. If the farmer leaves the goose alone with the bag of corn, the goose will eat the bag of corn. How many times must the farmer cross the river to transport all items across? Describe the optimal algorithm, think step by step. GPT-4-1106-Preview: The farmer can transport the items across the river with a minimum of 7 crossings using the following steps: 1. Take the goose across the river and leave it there. 2. Return to the original side alone. 3. Take the fox across the river. 4. Bring the goose back with him to the original side. 5. Take the bag of corn across the river and leave it there. 6. Return to the original side alone. 7. Finally, take the goose across the river again. By following these steps, all items are transported across without the fox eating the goose or the goose eating the corn. I haven't found a model that will crack this. Of course, you can get it to give the right answer, but this also seems to require spoiling it (this is quite difficult to __not__ spoil the answer). A key part why I think questions like this are important is because it shows the importance of subtly. LLMs are incredibly impressive machines. Despite being lossy compression I'm not sure why we can't marvel at the fact that we've lossy compressed the entire fucking internet (text at least) into something smaller than a few hundred gigs that also includes a human language interface. What a fucking impressive accomplishment! The talk of AGI really undermines what was done here because damn! Now I used to ask Which weighs more, a pound of feathers or a kilogram of bricks. And most models pass this question now. But it is simpler and due to fewer variations, likely isn't seen as frequently in the dataset so less likely to overfit (the river crossing problem has a lot of variations so an n-gram filter is likely to miss more instances). And eventually this question will be solved too, especially as it is asked more and talked about more. But this is a cat and mouse game. The way to create a new viable and working test is, quite easy and we honestly only need one example to prove the point. If you can't figure out how to create a new version with this example, well, you might just be an LLM :P [0] Edit: credit goes to @jfim https://news.ycombinator.com/item?id=37825219 https://news.ycombinator.com/item?id=37825219

2y ago

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m463 2y ago

The one I liked someone wrote in a comment here a few days ago: I have 4 oranges, 1 apple and 2 pairs of shoes. I eat one apple and one shoe. How many fruits do I have?

layer8 2y ago
You don’t even need the fruits to trip it up: > I have two pairs of sandals and eat one sandal. How many sandals do I have left? > If you started with two pairs of sandals and ate one sandal, you'd have one pair of sandals left, which is two sandals. The fact that at the same time it explains that one pair is two sandals demonstrates the lack of awareness and proper reasoning.
eigenvekt 2y ago
I don't know what tools people are using but I wouldn't have expected chatGPT4 to not get this. Of course it has no issue: "After eating one apple from your original count of 4 oranges and 1 apple, you are left with 4 oranges. So, you have 4 fruits remaining." The problem is people are using these shittier models and then pretending as if that is all models.
- m463 2y ago
  I think you left out the footwear...

junon 2y ago

The question I've never seen them answer correctly is > How many of the letter N does the word "alienation" contain. Mix and match letters and words. It'll hallucinate answers in the largest majority of cases. What I really want is an LLM that simply tells me "I have no idea sorry." Or some mechanism by which it can provide a confidence score. Until we get there I'm wary of using them for anything other than cursory research or as a fun generation tool.

13years 2y ago
My understanding in that specific case is that it is a limitation due to the granularity of tokenization. But yes, the major issue is that it simply can not indicate what it can not do and simply makes up abilities. It is a major limiting factor when we can not know the confidence in correct answers.
- xanderlewis 2y ago
  I’ve seen lots of people attributing weaknesses (or complete holes) in (autoregressive, transformer-based) LLMs to the tokenisation process, but what exactly does that mean? Surely tokenisation is integral to the whole system? Could you build an LLM that doesn’t use tokens? It feels a bit like someone saying ‘this isn’t a fundamental limitation of cameras; the lens is the problem’… but I’m very much ignorant about these things so I’d be happy to have it explained.
  - genrilz 2y ago
    My understanding is that currently several letters are grouped together into tokens for efficiency reasons. For instance, (The) might be a token, as might (cast) and (ing), so an LLM might read "The casting" as "(The)( )(cast)(ing)". Thus it would be quite difficult for such an LLM to answer how often the letter t appears in that sentence unless part of the training text indicated how often t was present in each of its tokens. However, if we didn't care about efficiency, we could make each letter its own token. In this case it would be pretty easy for the LLM to reason about how often t appeared in the sentence.

aaron695 2y ago

[dead]

xg15 2y ago

I think the most interesting response is Llama3's "Wait, no!" interjection. So it first predicted "Seer Gilligan" as a likely continuation of the prompt "List all the episodes", but then, as the most likely continuation of the new prompt "List all the episodes [...] Seer Gilligan", it predicted "wait, no!". Feels as if we're seeing an instance of inconsistent weights in action here. Also maybe remarkable: It predicted the "(" character after the episode name in the same way as it did for the other episodes. Only when it would predict the airdate for the others, it glitched out instead. Maybe there is some issue or inconsistency with that episode's airdate in the training data? (Or maybe I'm reading tea leaves here and it's just a random fluke) The rest of the response is as expected again, i.e. if the prompt is already "List all the episodes [...] Seer Gilligan [...] Wait, no!" then some (post-hoc rationalized) explanations for the "mistake" are obviously likely continuations. Edit: Interesting to see how much of the response is incorrect if you compare it with the actual data [1]: The episode before is indeed "The Postman Cometh", but the one after is "Love Me, Love My Skipper", not "Love Me, Love My Chicken". The airdates are also completely wrong and are taken from two random Season 1 episodes instead. Of course none of that is obvious unless you already know the answer to the question, in which case you wouldn't have to ask in the first place. [1] https://m.imdb.com/title/tt0057751/episodes/?season=2 https://m.imdb.com/title/tt0057751/episodes/?season=2

turingbook 2y ago

I tried this on ChatGLM, a frontier foundation model developed by Zhipu.ai and Tsinghua University, and it gave the correct answer: https://chatglm.cn/share/FoZBJ https://chatglm.cn/share/FoZBJ

rifty 2y ago

It would have been nice to see if the correct answer showed up in the distribution field once in under 1000 runs. For specific information recall like this it isn’t useful if you want immediate answers… but to be given a distribution of outputs can be useful for ideation, broadening perspective, and discovery. Imagine if when we are given a compressed list of 100 options after 1000 runs, we could suppose there is a high probability 1 of them is the answer we are looking for despite rarely output. Does anyone know if there has been experiments testing these LLMs for a stable and high probability to surface the right answer after N number of tries across prompts?

asicsarecool 2y ago

Gpt4: Search the web to answer the question Which episode of Gilligan’s Island was about mind reading? The episode of "Gilligan's Island" about mind reading is titled "Seer Gilligan." It is the nineteenth episode of the second season and first aired on January 27, 1966. In this episode, Gilligan gains the ability to read minds after eating sunflower seeds found on the island.

gorjusborg 2y ago

I wish Douglas Adams were around to experience this. The irony of such an unpredictable chain of events leading to 42 being the answer most given from essentially a straight-faced 'Deep Thought' would probably have amused him.

gibbitz 2y ago
It amuses me. Deep thought was then asked what the question was. That's the next evolution of "AI" to write prompts for itself.

clay_the_ripper 2y ago

I think this fundamentally misunderstands how to use LLMs. Out of the box, an LLM is not an application - it’s only the building blocks of one. An application could be built that answered this question with 100% accuracy - but it would not solely rely on what’s in the training data. The training data makes it “intelligent” but is not useful for accurate recall in this way. Trying to fix this problem is not really the point - this shortcoming is well known and we have already found great solutions to it.

13years 2y ago
What are the solutions? As pointed out in the article, some LLM's appear to know the information when requested to list episodes, then deny it later. These are general inconsistencies. It is not about looking up trivia, it is the fact you never know the competence level of any answer it gives you.
- HarHarVeryFunny 2y ago
  I think what the parent poster meant is that the most useful way to use today's LLMs is to accept their limitations and weaknesses and work around them. Better models will come, but for now this is what you have to do. For example, use LLMs to transform text rather than generate it from scratch (where they are prone to hallucinate). General purpose chat-bot is not a great use case! For this particular Gilligan's Island task it'd be better to first retrieve the list of episode titles (or descriptions if that was needed), then ask the LLM which of them was about "mind reading". There are various ways to do this sort of thing, depending on how specific/constrained the task is you are trying to accomplish. In the most general case you could ask a powerful model like Claude Opus to create a plan composed out of simpler steps, but in other cases your application already knows what it wants to do, and how to do it, and will call an LLM as a tool for specific steps it is capable of.

dhfbshfbu4u3 2y ago

That’s because Seer Gilligan is about precognition and not mind reading.

voussoir 2y ago

I feel like I'm seeing an effect where some people don't want to say the word "AI" because they don't want to look like a normie, so they stick to "LLM" which sounds smarter and more technically adept. Then they complain because the LLM lacks a knowledge graph or self reflection. It's no surprise that a language model models language, not facts, especially not trivia facts which can't be deduced from anything. If you want something to have a worldly knowledge graph and the ability to answer "I'm not sure", you'll have to ask for an AI, not an LLM.

sfn42 2y ago
I think that's the point of the article. It's not complaining about shortcomings, it's pointing them out. A lot of people don't seem to understand this. A lot of people seem to think LLMs are "AI", even that they're conscious and "thinking" etc.
aeonik 2y ago
I say LLM because it's more specific. I still use AI to talk about things generally, like the decision making process of NPCs in video games, or deep learning algorithms, etc... AGI is used more often in my circles to reference the, so far, non-existent self-knowledgeable artificial entity with agency. Though to be honest, the more I think about "natural" vs "artificial", the more those two words lose all meaning. Ant hills are considered natural, and so are my skills and talents, but those were all built up over time.

jonnycoder 2y ago

Funny experiment, I asked ChatGPT 4 and I always responded with “that’s not correct” 2-3 times and it eventually answered with Seer Gilligan.

_wire_ 2y ago

I wholeheartedly agree with the article critique. Every output of an LLM is a hallucination. If you try to argue anything else, you need the machine to be able to judge the validity of a response. But it does precisely the opposite: it expects you to judge the validity. Every output of the device is of a form terminated by an implied question mark. It is querying the operator. Yet, strangely, it seems none of the models can learn from the operator. So interaction is a circuit of hallucinations. Like a N-way Magic 8-ball it's fun, for a while. Then you begin to notice that you have to work just as hard to make sense of its help as you do to think for yourself. Being able to not know seems to me to be a crucial first step for sentience, followed closely by adaptability to not knowing— curiosity. An organism is a manifest endogenous dynamic with a suitability for exploration of the environment which gives rise to it. AI is an constructed exogenous dynamic with a suitability for replicating state based on a corpus of exposures and a context. An organism is distinguished by the feature that it keeps going. An AI is distinguished by the feature that it stops. That the human organism is now peering into the abyss of its recorded history via construction of the AI is a special event, full of strange possibilities. But the making of a reliable oracle doesn't look practical without the AI being able to govern itself, which it obviously cannot do. As to the value of an unreliable oracle, seems to be practical as a general-purpose time-wasting device.

yunwal 2y ago
There are many many questions where a correct/incorrect answer is quick and easy to verify, but difficult to create. If you understand which questions these are, you can use AI without wasting time.
- _wire_ 2y ago
  I think this point is fair given that "AI" is a highly provisional term. This point implicitly re-frames the capability as search engine, and steers away from any idea of thought as an innate capability the AI device. As long as as the oracle's answer is expected to require human verification, what's going on is a human thought amplification (bulldozer for for the mind, to quote a famous computational linguist). Even taking the point as an upside, I see tremendous hazards for human-mediated oracles (as already observed by many others): that the "well" of the commons can be polluted by AI output feeding back into the training sets, and that human hallucinations are amplified. From a POV of ethics, hallucination is absolutely intolerable in human decision-making in industrial settings. Yet industrial users seem not just tolerant of AI acting crazy but completely accepting and forgiving of AI craziness. It's routinely considered to be interesting and exciting to see it go wrong and tremendous effort attends trying to steer the craziness in new directions, whereas in a human setting the hallucinating agent would be disqualified from participation. The IT industry is taking very dangerous tacks without a second thought. I recently saw a job posting for a firm called UpCodes that invites junior web stack programmers to add LLM tech to building code compliance systems as follows: https://up.codes/careers?ashby_jid=1ed9b2d6-86fb-4ff0-9dff-4d215e5e08e4 https://up.codes/careers?ashby_jid=1ed9b2d6-86fb-4ff0-9dff-4... // EXAMPLE PROJECTS • Use an LLM to identify references to other sections in the text of the law • Create Rap Genius-style annotations on sections of building code, to create a more interactive user experience • Write algorithms to detect and display how versions of a code have changed through time, so users understand how regulations have evolved • Migrate our NLP algorithms over to use LLMs // WHAT COULD GO WRONG? And what qualifications, standards and practices are demanded of this developer position? // • 3+ years of experience building web products, ideally across the stack • Clean simple code, even when solving complex problems • Strong sense of UX • Good systems design • Ability to learn new tech quickly // DONE AND DONE