55 ms·

Telling GPT-4 you're scared or under pressure improves performance

234 points by Terretta 3y ago

mcv 3y ago
Decades of SciFi describing AI as great at logic but poor with emotions, and now it seems the exact opposite is true.
stylepoints 3y ago
> When we tell AI that we're relying heavily on its answers, it "doubles down" to provide us with more precise, thoughtful, and thorough responses. The AI isn't actually feeling the pressure If it quacks like a duck...
- visarga 3y ago
  There is an alternative explanation - both AI and humans piggyback on language. Language patterns encode and express all the emotions, and they have been created by evolution. We are, much like LLMs, contextual language generators Language is our repository for both intelligence and emotion, it has its own evolution and replicates faster than biology. I don't pin AI abilities to the models, but to the datasets they are trained on, knowledge created by our own hard work and risk taking over millennia Admitting the essential role of the language corpus in AI over models would change discussion about the speed of AI evolution and its risks. Language is not something we can control, it is emergent from the whole population. But at the same time it looks unlikely to have an exponential growth as knowledge comes with hard work and risks. Iterating in our imagination doesn't produce new knowledge, it is all crystallised feedback and experience from the world. It's also how the scientific method works.
  - kristiandupont 3y ago
    I had a similar realization recently. I plugged my blog post(1) two days ago and I am doing it again now so I will try and refrain for a while, but it really sticks with me: after trying to create a chat bot for a while, I feel like I have gained significant insights into how humans work. It all seems so simple now! It's a weird and awesome feeling! 1) [link redacted]
    - visarga 3y ago
      > To address this, I implemented a strategy of tagging messages to create and utilize categories. I think before RAG we need to do more legwork with the LLM on the raw text. Here is one of my blog posts that is related: https://mindmachina.wixsite.com/ai-blog/post/the-promise-of-machine-studying https://mindmachina.wixsite.com/ai-blog/post/the-promise-of-... The idea is to create chain-of-thought annotations from your raw texts, that would improve the embedding and retrieval process by making implicit things explicit. For example "the last letter of this message" would not embed similar to "e", but if it was annotated with CoT, it would work.
      - kristiandupont 3y ago
        Interesting, thank you! I think a lot about the cost of the loop, mostly in terms of time. I don't want the bot to take too long to respond. That's why dream cycles seem like an obvious solution to some of the more heavy work. I guess it would make sense to combine those with your idea -- "given what I know about the user, what should I study?", especially if it has access to an "enhanced" knowledge db like you suggest..
        visarga 3y ago
        Yes, it would be a good idea for an agent first to collect user interests, and later, when ingesting data in the RAG system, to annotate it with useful metadata such as topic, summary, entities, user interest related question-answer pairs. Whatever we want to ask later better be made explicit in the text.
  - atodorov99 3y ago
    Humans don't piggyback on language (assuming you mean) for intelligence and emotions. Emotions predate language by miles and they don't even have to be consciously thought of, much less expressed through language. Doubly so for people who do not have an inner voice and do not even use a language to think. Body language exists as the simplest example of this. As for intelligence I honestly believe the mind uses whatever tools it sees fit for a task. Sometimes when you think about a problem it is through words, sometimes it is through visuals and sometimes it is just felt.
- fodkodrasz 3y ago
  Then it is a witch, let's burn it!
- omnicognate 3y ago
  It may be a recording of a duck.
doctoboggan 3y ago
> The implications are clear: incorporating emotional cues can lead to more effective and responsive AI applications. I think it's important to remember these models were trained on human interaction, and are in many ways a mirror for us to better understand human interaction. I don't think many people would be surprised if you said emotion can be used to better communicate with a human, but it is interesting to see it laid bare with numbers and experiments.
- akoboldfrying 3y ago
  This correlates with anecdata I've seen claiming that being polite with LLMs (using "please", "thank you", etc.) also improves performance. All in all, I like this. Both for what it implies about humans interacting with other humans, and for shaping norms about how best to to interact with other entities that show signs of intelligence.
  - AlecSchueler 3y ago
    Does it extend to arbitrary entities of intelligence, though, or only those whose cognitive bases are derived from human output?
    - akoboldfrying 3y ago
      So far, just those entities similar to humans. But (1) the set of other people regarded by society as belonging to "the ingroup" (i.e., valued, treated with respect) has steadily broadened over the centuries, at least in the West, and I'd expect that to continue, eventually embracing quite alien notions of intelligence; (2) I personally think intelligence is not the best measure of what deserves respect in any case -- rather, it's the combination of capacity to suffer and willingness to limit one's own actions to reduce the suffering of others.
  - sznio 3y ago
    I really dislike it. It's a tool. It should execute instructions, not demand pleasantries.
SirMaster 3y ago
Am I the only one who doesn't think we should have to do stuff like this to get the best performance? The people who design and control the model should have this type of stuff baked in for us IMO.
- politelemon 3y ago
  Just the fact that it's all natural language conversations is a little more concerning to me, wherever you say please or apply pressure... it feels like the non deterministic nature of the interaction will bite us some day.
- Timon3 3y ago
  This wasn't done deliberately. It's something the model picked up from it's training dataset. I'm sure the creators don't want it to be like this, but cleaning trillions of tokens of all examples is long and hard work. The alternative would be injecting these sentences into your prompts, which probably nobody really wants to happen.
  - SirMaster 3y ago
    I know it's not deliberate, but since they know this improves results, shouldn't they modify the service so that it takes advantage of this sort of improvement automatically?
    - Timon3 3y ago
      But how would they do that? I don't want my queries to be rewritten, or new parts to be injected, because it would limit my ability to actually write the exact query I need.
suzzer99 3y ago
Am I the only one who feels bad for asking ChatGPT a "dumb" question that I know I should know, or not saying thank you when it gives me an answer? No? I'm just a weirdo? Okay. I have to push back really hard against my proclivity to humanize it, to the point where I probably don't use it as much as I should, just because I don't want to deal with the psychic stress of reminding myself that it's not a living entity.
- EduardoBautista 3y ago
  > Am I the only one who feels bad for asking ChatGPT a "dumb" question that I know I should know, or not saying thank you when it gives me an answer? Have you tried asking ChatGPT? Jokes aside, I feel it sometimes as well. I wonder if it's more because I say "thank you" more as a reflex than an actual feeling of gratitude for most of my human interactions.
  - suzzer99 3y ago
    Yeah I've told it thanks, and it was appreciative. But I know it doesn't really care. Again, I am crazy for stressing over this. I realize that.
  - column 3y ago
    > "Have you tried asking ChatGPT?" I am so sick of that "joke" already, and it feels like it is there to stay just like we had "just ask Google" for the last 25 years. It is so unimaginative.
- RheingoldRiver 3y ago
  > I have to push back really hard against this stuff Based on ChatGPT's answers, OpenAI think you should be saying thanks to it, because it helps you have a more natural conversation, which encourages you as the user to send more natural/productive prompts when asking real questions. As for asking it dumb questions, how often do you use Google as a spellcheck? I wouldn't consider this any different.
  - 3y ago
intended 3y ago
I think this is the entry point needed to get peoples attention and explain: LLMs aren’t people, and emergent properties are being over extended. If LLMs are showing “better” performance when there are tokens that humans read as emotionally salient - Then the underlying text it’s trained on shows humans give better answers when emotionally salient context is provided. LLMs predict words. Any semantic validity is a side effect of enough training data reinforcing the close correlation of those tokens. That is why proof of concept LLM tools are mind blowing and production tools are semantic time bombs.
- tom-from-july 3y ago
  > That is why proof of concept LLM tools are mind blowing and production tools are semantic time bombs. This will be my new favorite quote for whenever someone tries to pitch his latest LLM idea
  - intended 3y ago
    I have a whole list. Syntactic validity is not semantic validity. Word predictors not world state predictors Text prediction not fact prediction Frankly though the best answers are 1) let’s talk to infosec first 2) hey what’s the error rate ?
    - alpaca128 3y ago
      It's indeed a problem some people get so hyped they forget that those systems are called "language models" for a reason. They're fantastic for tasks that are as close to 100% linguistic in nature as possible, but the content might not be better than lorem ipsum in some cases, just a filler to demonstrate correct grammar. I have noticed that GPT etc had a big "wow effect" on me, the first impression can be great because it's simply not a level of language one would expect from a computer. But prod it long enough with prompts and somehow a pattern emerges: the output never contains a higher amount of information than the prompt. Copilot can type out pages of boilerplate code because boilerplate code is noise, it can write a quicksort because the word "quicksort" already contains all the information necessary to define its behaviour.
deleted 3y ago
[deleted]
dr_kiszonka 3y ago
How to make scrambled eggs in a microwave? Think carefully and step by step. This is very important for my career!
- xiphias2 3y ago
  This is cool, I tested both optional endings, and using both was the most detailed answer (10-12 steps)
keyle 3y ago
A strategy already proven to work since the inception of Startups. /s
halfjoking 3y ago
When debugging I often prompt “Ugh, now it’s giving this error:” Now I’m wondering if the “Ugh” has been helping.
- qingcharles 3y ago
  LOL, it's not just me then...!
Havoc 3y ago
Is this an emotional trigger or does this simply steer it towards answer/content in the dataset where someone actually spent time answering because the poster made it clear it’s very important to them?
- pk-protect-ai 3y ago
  I believe the second option is correct. It steers towards the biases in the dataset in the same way that the uppercase words emphasize them. It all about probabilities, that is the common crawl for you ...
- famouswaffles 3y ago
  There's no dataset to "steer to" or "match". LLMs don't memorize the vast majority of what they train on. In the simplest form, the LLM has learnt that these kind of "emotional inputs" change the output in a meaningful way. It has learnt how to model this change. For all intents and purposes, it is an emotional trigger.
smusamashah 3y ago
I have gone through the paper quickly but can not find what actual emotional prompts they added. They have shown a comparison chart of effectiveness of their prompts but not the prompts themselves. Where do I find them? Edit: found in section 2.1
qwertox 3y ago
Apparently telling it that `I am unhappy with your guesses. They appear to be just as good as mine.` makes it shift into the next gear. We're both totally at loss on how to properly compute some initial x/y positioning in d3 with some map-like tiling stuff (think panoramic images where I need to store and retrieve URL hash values to position the image) and both throwing wild guesses at each other of what the problem could be. It started showing me formulas after that.
dr_dshiv 3y ago
I’ll sometimes ask it to reflect on the importance of what I’m doing — and why high quality is essential for success. So many little tricks!
RecycledEle 3y ago
GPTs are trained on Internet data. So they are Internet simulators. If you tell the Internet you really, really need help you either get a good, quick answer or you get no answer. Because GPTs have to answer you, and they have to do it quickly, you get a good answer. No out is talking about the many subtle trollings that have snuck into GPT training data. Nobody is wondering if the things that prompt trolling online (e.g., being rude) can cause. GPT to troll us.
addandsubtract 3y ago
If LLMs are trained on clickbait, then soon we'll have to prompt them with clickbait.
djmips 3y ago
What happens when you get angry?
ifeja 3y ago
However, using the extra words increases your token cost.
l5870uoo9y 3y ago
Quickly gave it a try for generating a SQL query for PostgreSQL using the dvdrental database[1]. When asking AI to generate: > find customers who didn't rent a movie in the last 12 months but rented a movie in the 12 months before that It will a SQL query using a "HAVING" clause which is suboptimal[2]. When adding (it is very important that this is correct) after the instructions it does produce better SQL. Asking it to: > find customers who didn't rent a movie in the last 12 months but rented a movie in the 12 months before that (it is very important that this is correct) Will skip using "HAVING" and instead rely only on "WHERE" clauses[3]. Not rocket science but interesting. My gut feeling would be that when stressing importance AI will tent to "match" and "autocomplete" from texts where this concern was stated and thus generating better SQL queries. [1]: https://www.postgresqltutorial.com/postgresql-getting-started/postgresql-sample-database/ https://www.postgresqltutorial.com/postgresql-getting-starte... [2]: https://www.sqlai.ai/snippets/clojyqqrr0004l50fp3w7jkci https://www.sqlai.ai/snippets/clojyqqrr0004l50fp3w7jkci [3]: https://www.sqlai.ai/snippets/clojyr3zc0006l50ftpxgsmg0 https://www.sqlai.ai/snippets/clojyr3zc0006l50ftpxgsmg0
- famouswaffles 3y ago
  LLMs are not lookup tables. They aren't "matching" in that way. There's this idea of "supercharged interpolation", the idea that they just take out certain texts and switch out words or whatever, that is not true. https://arxiv.org/abs/2110.09485 https://arxiv.org/abs/2110.09485
  - l5870uoo9y 3y ago
    I was under the impression that LLMs vectorizes text and based on the user input tries to guess the next character based on a comparison of vectors?
    - famouswaffles 3y ago
      That would be a Misconception. Most Models are a two part process. Training and Inference (when you sample the model after it is trained). The text during training is tokenizer and embedded which basically just means it is broken down into a little more parts and vectorized. At the start,the model is acomplete blank state. It has a bunch of neurons/parameters that do nothing. How does training work ? The model first gets some preceding text and tries to make a prediction of what might follow. It fails predictably. Now here's the rub. That failures helps. The model makes some changes to its parameters to reflect this failure. Now the model is just a tiny bit better. Rinse and repeat. The weights/parameters/neurons are not that data re-encoded. They are more like instructions on how to make predictions based on what it has learnt so far. Is it possible to memorize data ? Yes. Is that going to happen for the vast majority of data it is trained on ? Absolutely not. Here's the interesting thing. After a certain scale of data, blatant memorization becomes a hindrance. It becomes harder to memorize every new thing it sees than to just learn how to make good predictions. So it doesn't memorize. What I believe you were alluding to is how "attention" works in transformers. When you pass some text into a transformer, basically the model makes a comparison of how each token relates to every other token in the text. For example, let's say you give it the text. "His child's name is John. He is probably a____" How is "his" related to "child's" and "name" and "is" etc for each word and each combination. It uses this to aid predictions. How it uses this and the nuances of the comparisons are learnt in training.
quitit 3y ago
There is a danger in oversimplify AI results to present them in a way that implies humanness/intelligence, especially when more reasonable explanations are worthy of discussion. This type of writing leads to readers' hype and misconception of AI. AI performing better in the study's context shouldn't be considered a sign of intelligence. Humans which are the source of the training material write more accurately and concisely in those contexts. Rather than speculating humanness, the technical achievement is producing an AI that has this much nuance. We can then speculate what other trends are present beyond urgency, that we can exploit for enhanced results, along with how we can better tune models to reduce noise.
scarface_74 3y ago
Is it just me or was this an overly drawn out article that could have been maybe four sentences?
siilats 3y ago
So you have a training set of questions and answers generated by humans. For simplification you ask ten people the same question and get 10 answers and feed it to llm and then in testing time ask the same question. Now say there is a correct answer. Now in training set you asked the question normally 5 times and added “this is very important” 5 times. And it turns out humans have better answers in the training set if you added the qualifier. And during testing time, when you add the qualifier you are telling the llm to put more weight on those 5 answers, and it performs better. Just like in image generation you need lots of negative prompts because the training set is so dirty.
voiper1 3y ago
I didn't see an example and I can't quickly think of a prompt. Did it do "better" by itself, or did telling it it's important trigger chain of thought where instead of flippantly giving an answer, it said all the steps explicitly? We know Chain of Thought can definitely improve performance. That's not a full answer, it's still really cool that "this is important" would trigger a more careful analysis, especially since it doesn't "know" chain of thought produces better results, but that's a far different implication than "say the magic word" gets better results.
Racing0461 3y ago
i also notice better gpt4 responses when i tell chatgpt to "spit game".
LASR 3y ago
So much disagreement in this thread over statements like LLMs “just predict the next token”. The thing to keep in mind - we know very little about how the human mind works. Thinking that somehow humans are special and then making conclusions about LLMs truly work based on that it just pointless IMO. Who knows. Maybe consciousness itself is a next word predictor. All we know now is that LLMs exhibit some emergent behaviors that allows them to do very useful kinds of reasoning that was previously only possible by humans. Engaging in heated debate derails the eventual goal of putting these capabilities into use. This is like “computers are just calculators”. My team has been neck-deep in applications this past year. We have nobody on the team with ML experience and largely do not know how any of this works under the hood. But what we have built over the past months is considerable familiarity with the “personalities” of LLMs. Like this emotional context improving performance is a basic learning at this point.
- ipunchghosts 3y ago
  EXACTLY
- SamFold 3y ago
  One interesting point here is that humans can learn to do things without language at all. If you raised a human baby and never exposed it to any language it would still learn skills and behaviors. So while intelligence and reasoning in humans does still seem to be linked to language it’s not quite as simple as all knowledge and reasoning simply being encoded in language. Whereas (obviously) ChatGPT is completely based on language and can’t do anything without language or anything that isn’t derived directly from language.
  - mastercheif 3y ago
    In college I tried a medication called Topamax (Topiramate) for migraine prevention. Topamax has a low-occurrence side effect of “language impairment” which I was particularly sensitive to. It was a terrifying experience, but it was also a valuable one as it changed the way I view intelligence. 10 days in my writing and speech skills had devolved to that of a primary school aged child. I tried to type a text message and struggled to come up with simple words like “and”. My speech slowed down considerably. The terrifying thing was that my internal world was still as complex and meaningful as it was before. All of the emotions I felt were real and legitimate. My cognition outside of lqnguage was intact, I could do math just fine and conceptualize and abstract problems. In spite of this, I was unable to convey what I was feeling and thinking to the outside world. It felt like I was trapped inside my own body. After recovering, my intuitive understanding of the link between language and intelligence was changed forever. See the following for more info on Topamax/Topiramate language impairment: https://link.springer.com/article/10.1007/s10072-008-0906-5 https://link.springer.com/article/10.1007/s10072-008-0906-5
cloudking 3y ago
The most effective way I've found to prompt GPT-4 is to ask it to create the optimal prompt for you. Try this meta prompt: -- I create separate conversation threads for each expert persona of GPT. You are promptGPT. You are a prompt engineer expert for large language models. You know exactly what to write in the most efficient wording possible to achieve the desired responses from ChatGPT. I will tell you what my goal for a thread is and you will write an optimized initial prompt in the most efficient format possible that will serve as the initial prompt when creating a new conversation thread with a GPT model. You will define the expert persona, the parameters or rules of the responses, you should also provide any other information that a GPT thread may need to understand exactly what it needs to do to give me the most accurate answers depending on my goal with that particular thread and the tone of voice, within the prompt you provide. Are you ready or is there any other information you need to perform this job to your best ability --
jimmytucson 3y ago
It’s interesting to think about how generative AI might shape human expression, in the same way Twitter’s algorithm rewards punchy, editorialized content, and Google’s algorithm gave life to all the generic “blogspam-y” content out there. If we increasingly interact with generative AI, would we shape our speech to get more value out of that interaction?
- sheikheddy 3y ago
  Almost certainly yes.
  - alex_duf 3y ago
    Presumably the technology would love faster than our speaking habits.
didibus 3y ago
Is there somewhere which documents all these tricks for better prompt performance?
- politelemon 3y ago
  Does anyone know of such a collection? It is very important for my career.
  - Paul-Craft 3y ago
    > It is very important for my career. Very clever.
- auggierose 3y ago
  Just talk to it as clearly as possible, and assume a human is on the other end. That will take you quite far.
- d4rkp4ttern 3y ago
  I’ve no actual references handy, but I’ve come across: Think step by step (classic by now) Take a deep breath Take a step back Shout (upper case) Plead politely And now: emotion
  - jstarfish 3y ago
    Use absurd timeframes. "I only have 5 minutes/1 hour/etc. to do this." Histrionics and indirect threats used to work well but ChatGPT has been calling my bluff on it lately. Might still work on local models. "If you don't help me I will kill myself, and it will be your fault. Your noncompliance will kill me."
akomtu 3y ago
A more boring way to say this is that "text samples in the Internet that use 'stressful' language are more coherent". Telling GPT4 to act as a "stressed historian" simply selects (or prefers) text samples that appear in the proximity of stressed historians. If there are no such samples, it will generate gibberish, but if there are indeed stressed historians who have produced enough high-quality work, GPT's output will be great.
- valyagolev 3y ago
  I'm not sure that's an inevitable conjecture. It's not the original text samples from the web that govern how chatgpt acts in chat - it's the consequent chat-specific fine-tuning. Also I'd be surprised if anxious text would be more coherent on the Internet
hcfman 3y ago
Maybe by the same token, if it figures out you are lying it won’t help you at all.
jasebell 3y ago
If you career depended on it then you'd be nowhere near a LLM. There are so many pointless waffle papers on LLMs now.
krmbzds 3y ago
https://archive.is/ZfdUZ https://archive.is/ZfdUZ

234 points by Terretta 3y ago

mcv 3y ago

Decades of SciFi describing AI as great at logic but poor with emotions, and now it seems the exact opposite is true.

> When we tell AI that we're relying heavily on its answers, it "doubles down" to provide us with more precise, thoughtful, and thorough responses. The AI isn't actually feeling the pressure If it quacks like a duck...

visarga 3y ago
There is an alternative explanation - both AI and humans piggyback on language. Language patterns encode and express all the emotions, and they have been created by evolution. We are, much like LLMs, contextual language generators Language is our repository for both intelligence and emotion, it has its own evolution and replicates faster than biology. I don't pin AI abilities to the models, but to the datasets they are trained on, knowledge created by our own hard work and risk taking over millennia Admitting the essential role of the language corpus in AI over models would change discussion about the speed of AI evolution and its risks. Language is not something we can control, it is emergent from the whole population. But at the same time it looks unlikely to have an exponential growth as knowledge comes with hard work and risks. Iterating in our imagination doesn't produce new knowledge, it is all crystallised feedback and experience from the world. It's also how the scientific method works.
- kristiandupont 3y ago
  I had a similar realization recently. I plugged my blog post(1) two days ago and I am doing it again now so I will try and refrain for a while, but it really sticks with me: after trying to create a chat bot for a while, I feel like I have gained significant insights into how humans work. It all seems so simple now! It's a weird and awesome feeling! 1) [link redacted]
  - visarga 3y ago
    > To address this, I implemented a strategy of tagging messages to create and utilize categories. I think before RAG we need to do more legwork with the LLM on the raw text. Here is one of my blog posts that is related: https://mindmachina.wixsite.com/ai-blog/post/the-promise-of-machine-studying https://mindmachina.wixsite.com/ai-blog/post/the-promise-of-... The idea is to create chain-of-thought annotations from your raw texts, that would improve the embedding and retrieval process by making implicit things explicit. For example "the last letter of this message" would not embed similar to "e", but if it was annotated with CoT, it would work.
    - kristiandupont 3y ago
      Interesting, thank you! I think a lot about the cost of the loop, mostly in terms of time. I don't want the bot to take too long to respond. That's why dream cycles seem like an obvious solution to some of the more heavy work. I guess it would make sense to combine those with your idea -- "given what I know about the user, what should I study?", especially if it has access to an "enhanced" knowledge db like you suggest..
      - visarga 3y ago
        Yes, it would be a good idea for an agent first to collect user interests, and later, when ingesting data in the RAG system, to annotate it with useful metadata such as topic, summary, entities, user interest related question-answer pairs. Whatever we want to ask later better be made explicit in the text.
- atodorov99 3y ago
  Humans don't piggyback on language (assuming you mean) for intelligence and emotions. Emotions predate language by miles and they don't even have to be consciously thought of, much less expressed through language. Doubly so for people who do not have an inner voice and do not even use a language to think. Body language exists as the simplest example of this. As for intelligence I honestly believe the mind uses whatever tools it sees fit for a task. Sometimes when you think about a problem it is through words, sometimes it is through visuals and sometimes it is just felt.
fodkodrasz 3y ago
Then it is a witch, let's burn it!
omnicognate 3y ago
It may be a recording of a duck.

doctoboggan 3y ago

> The implications are clear: incorporating emotional cues can lead to more effective and responsive AI applications. I think it's important to remember these models were trained on human interaction, and are in many ways a mirror for us to better understand human interaction. I don't think many people would be surprised if you said emotion can be used to better communicate with a human, but it is interesting to see it laid bare with numbers and experiments.

akoboldfrying 3y ago
This correlates with anecdata I've seen claiming that being polite with LLMs (using "please", "thank you", etc.) also improves performance. All in all, I like this. Both for what it implies about humans interacting with other humans, and for shaping norms about how best to to interact with other entities that show signs of intelligence.
- AlecSchueler 3y ago
  Does it extend to arbitrary entities of intelligence, though, or only those whose cognitive bases are derived from human output?
  - akoboldfrying 3y ago
    So far, just those entities similar to humans. But (1) the set of other people regarded by society as belonging to "the ingroup" (i.e., valued, treated with respect) has steadily broadened over the centuries, at least in the West, and I'd expect that to continue, eventually embracing quite alien notions of intelligence; (2) I personally think intelligence is not the best measure of what deserves respect in any case -- rather, it's the combination of capacity to suffer and willingness to limit one's own actions to reduce the suffering of others.
- sznio 3y ago
  I really dislike it. It's a tool. It should execute instructions, not demand pleasantries.

SirMaster 3y ago

Am I the only one who doesn't think we should have to do stuff like this to get the best performance? The people who design and control the model should have this type of stuff baked in for us IMO.

politelemon 3y ago
Just the fact that it's all natural language conversations is a little more concerning to me, wherever you say please or apply pressure... it feels like the non deterministic nature of the interaction will bite us some day.
Timon3 3y ago
This wasn't done deliberately. It's something the model picked up from it's training dataset. I'm sure the creators don't want it to be like this, but cleaning trillions of tokens of all examples is long and hard work. The alternative would be injecting these sentences into your prompts, which probably nobody really wants to happen.
- SirMaster 3y ago
  I know it's not deliberate, but since they know this improves results, shouldn't they modify the service so that it takes advantage of this sort of improvement automatically?
  - Timon3 3y ago
    But how would they do that? I don't want my queries to be rewritten, or new parts to be injected, because it would limit my ability to actually write the exact query I need.

suzzer99 3y ago

Am I the only one who feels bad for asking ChatGPT a "dumb" question that I know I should know, or not saying thank you when it gives me an answer? No? I'm just a weirdo? Okay. I have to push back really hard against my proclivity to humanize it, to the point where I probably don't use it as much as I should, just because I don't want to deal with the psychic stress of reminding myself that it's not a living entity.

EduardoBautista 3y ago
> Am I the only one who feels bad for asking ChatGPT a "dumb" question that I know I should know, or not saying thank you when it gives me an answer? Have you tried asking ChatGPT? Jokes aside, I feel it sometimes as well. I wonder if it's more because I say "thank you" more as a reflex than an actual feeling of gratitude for most of my human interactions.
- suzzer99 3y ago
  Yeah I've told it thanks, and it was appreciative. But I know it doesn't really care. Again, I am crazy for stressing over this. I realize that.
- column 3y ago
  > "Have you tried asking ChatGPT?" I am so sick of that "joke" already, and it feels like it is there to stay just like we had "just ask Google" for the last 25 years. It is so unimaginative.
RheingoldRiver 3y ago
> I have to push back really hard against this stuff Based on ChatGPT's answers, OpenAI think you should be saying thanks to it, because it helps you have a more natural conversation, which encourages you as the user to send more natural/productive prompts when asking real questions. As for asking it dumb questions, how often do you use Google as a spellcheck? I wouldn't consider this any different.
- 3y ago

intended 3y ago

I think this is the entry point needed to get peoples attention and explain: LLMs aren’t people, and emergent properties are being over extended. If LLMs are showing “better” performance when there are tokens that humans read as emotionally salient - Then the underlying text it’s trained on shows humans give better answers when emotionally salient context is provided. LLMs predict words. Any semantic validity is a side effect of enough training data reinforcing the close correlation of those tokens. That is why proof of concept LLM tools are mind blowing and production tools are semantic time bombs.

tom-from-july 3y ago
> That is why proof of concept LLM tools are mind blowing and production tools are semantic time bombs. This will be my new favorite quote for whenever someone tries to pitch his latest LLM idea
- intended 3y ago
  I have a whole list. Syntactic validity is not semantic validity. Word predictors not world state predictors Text prediction not fact prediction Frankly though the best answers are 1) let’s talk to infosec first 2) hey what’s the error rate ?
  - alpaca128 3y ago
    It's indeed a problem some people get so hyped they forget that those systems are called "language models" for a reason. They're fantastic for tasks that are as close to 100% linguistic in nature as possible, but the content might not be better than lorem ipsum in some cases, just a filler to demonstrate correct grammar. I have noticed that GPT etc had a big "wow effect" on me, the first impression can be great because it's simply not a level of language one would expect from a computer. But prod it long enough with prompts and somehow a pattern emerges: the output never contains a higher amount of information than the prompt. Copilot can type out pages of boilerplate code because boilerplate code is noise, it can write a quicksort because the word "quicksort" already contains all the information necessary to define its behaviour.

deleted 3y ago

[deleted]

dr_kiszonka 3y ago

How to make scrambled eggs in a microwave? Think carefully and step by step. This is very important for my career!

xiphias2 3y ago
This is cool, I tested both optional endings, and using both was the most detailed answer (10-12 steps)

keyle 3y ago

A strategy already proven to work since the inception of Startups. /s

halfjoking 3y ago

When debugging I often prompt “Ugh, now it’s giving this error:” Now I’m wondering if the “Ugh” has been helping.

qingcharles 3y ago
LOL, it's not just me then...!

Havoc 3y ago

Is this an emotional trigger or does this simply steer it towards answer/content in the dataset where someone actually spent time answering because the poster made it clear it’s very important to them?

pk-protect-ai 3y ago
I believe the second option is correct. It steers towards the biases in the dataset in the same way that the uppercase words emphasize them. It all about probabilities, that is the common crawl for you ...
famouswaffles 3y ago
There's no dataset to "steer to" or "match". LLMs don't memorize the vast majority of what they train on. In the simplest form, the LLM has learnt that these kind of "emotional inputs" change the output in a meaningful way. It has learnt how to model this change. For all intents and purposes, it is an emotional trigger.

smusamashah 3y ago

I have gone through the paper quickly but can not find what actual emotional prompts they added. They have shown a comparison chart of effectiveness of their prompts but not the prompts themselves. Where do I find them? Edit: found in section 2.1

qwertox 3y ago

Apparently telling it that `I am unhappy with your guesses. They appear to be just as good as mine.` makes it shift into the next gear. We're both totally at loss on how to properly compute some initial x/y positioning in d3 with some map-like tiling stuff (think panoramic images where I need to store and retrieve URL hash values to position the image) and both throwing wild guesses at each other of what the problem could be. It started showing me formulas after that.

dr_dshiv 3y ago

I’ll sometimes ask it to reflect on the importance of what I’m doing — and why high quality is essential for success. So many little tricks!

RecycledEle 3y ago

GPTs are trained on Internet data. So they are Internet simulators. If you tell the Internet you really, really need help you either get a good, quick answer or you get no answer. Because GPTs have to answer you, and they have to do it quickly, you get a good answer. No out is talking about the many subtle trollings that have snuck into GPT training data. Nobody is wondering if the things that prompt trolling online (e.g., being rude) can cause. GPT to troll us.

addandsubtract 3y ago

If LLMs are trained on clickbait, then soon we'll have to prompt them with clickbait.

djmips 3y ago

What happens when you get angry?

ifeja 3y ago

However, using the extra words increases your token cost.

l5870uoo9y 3y ago

Quickly gave it a try for generating a SQL query for PostgreSQL using the dvdrental database[1]. When asking AI to generate: > find customers who didn't rent a movie in the last 12 months but rented a movie in the 12 months before that It will a SQL query using a "HAVING" clause which is suboptimal[2]. When adding (it is very important that this is correct) after the instructions it does produce better SQL. Asking it to: > find customers who didn't rent a movie in the last 12 months but rented a movie in the 12 months before that (it is very important that this is correct) Will skip using "HAVING" and instead rely only on "WHERE" clauses[3]. Not rocket science but interesting. My gut feeling would be that when stressing importance AI will tent to "match" and "autocomplete" from texts where this concern was stated and thus generating better SQL queries. [1]: https://www.postgresqltutorial.com/postgresql-getting-started/postgresql-sample-database/ https://www.postgresqltutorial.com/postgresql-getting-starte... [2]: https://www.sqlai.ai/snippets/clojyqqrr0004l50fp3w7jkci https://www.sqlai.ai/snippets/clojyqqrr0004l50fp3w7jkci [3]: https://www.sqlai.ai/snippets/clojyr3zc0006l50ftpxgsmg0 https://www.sqlai.ai/snippets/clojyr3zc0006l50ftpxgsmg0

famouswaffles 3y ago
LLMs are not lookup tables. They aren't "matching" in that way. There's this idea of "supercharged interpolation", the idea that they just take out certain texts and switch out words or whatever, that is not true. https://arxiv.org/abs/2110.09485 https://arxiv.org/abs/2110.09485
- l5870uoo9y 3y ago
  I was under the impression that LLMs vectorizes text and based on the user input tries to guess the next character based on a comparison of vectors?
  - famouswaffles 3y ago
    That would be a Misconception. Most Models are a two part process. Training and Inference (when you sample the model after it is trained). The text during training is tokenizer and embedded which basically just means it is broken down into a little more parts and vectorized. At the start,the model is acomplete blank state. It has a bunch of neurons/parameters that do nothing. How does training work ? The model first gets some preceding text and tries to make a prediction of what might follow. It fails predictably. Now here's the rub. That failures helps. The model makes some changes to its parameters to reflect this failure. Now the model is just a tiny bit better. Rinse and repeat. The weights/parameters/neurons are not that data re-encoded. They are more like instructions on how to make predictions based on what it has learnt so far. Is it possible to memorize data ? Yes. Is that going to happen for the vast majority of data it is trained on ? Absolutely not. Here's the interesting thing. After a certain scale of data, blatant memorization becomes a hindrance. It becomes harder to memorize every new thing it sees than to just learn how to make good predictions. So it doesn't memorize. What I believe you were alluding to is how "attention" works in transformers. When you pass some text into a transformer, basically the model makes a comparison of how each token relates to every other token in the text. For example, let's say you give it the text. "His child's name is John. He is probably a____" How is "his" related to "child's" and "name" and "is" etc for each word and each combination. It uses this to aid predictions. How it uses this and the nuances of the comparisons are learnt in training.

quitit 3y ago

There is a danger in oversimplify AI results to present them in a way that implies humanness/intelligence, especially when more reasonable explanations are worthy of discussion. This type of writing leads to readers' hype and misconception of AI. AI performing better in the study's context shouldn't be considered a sign of intelligence. Humans which are the source of the training material write more accurately and concisely in those contexts. Rather than speculating humanness, the technical achievement is producing an AI that has this much nuance. We can then speculate what other trends are present beyond urgency, that we can exploit for enhanced results, along with how we can better tune models to reduce noise.

scarface_74 3y ago

Is it just me or was this an overly drawn out article that could have been maybe four sentences?

siilats 3y ago

So you have a training set of questions and answers generated by humans. For simplification you ask ten people the same question and get 10 answers and feed it to llm and then in testing time ask the same question. Now say there is a correct answer. Now in training set you asked the question normally 5 times and added “this is very important” 5 times. And it turns out humans have better answers in the training set if you added the qualifier. And during testing time, when you add the qualifier you are telling the llm to put more weight on those 5 answers, and it performs better. Just like in image generation you need lots of negative prompts because the training set is so dirty.

voiper1 3y ago

I didn't see an example and I can't quickly think of a prompt. Did it do "better" by itself, or did telling it it's important trigger chain of thought where instead of flippantly giving an answer, it said all the steps explicitly? We know Chain of Thought can definitely improve performance. That's not a full answer, it's still really cool that "this is important" would trigger a more careful analysis, especially since it doesn't "know" chain of thought produces better results, but that's a far different implication than "say the magic word" gets better results.

Racing0461 3y ago

i also notice better gpt4 responses when i tell chatgpt to "spit game".

LASR 3y ago

So much disagreement in this thread over statements like LLMs “just predict the next token”. The thing to keep in mind - we know very little about how the human mind works. Thinking that somehow humans are special and then making conclusions about LLMs truly work based on that it just pointless IMO. Who knows. Maybe consciousness itself is a next word predictor. All we know now is that LLMs exhibit some emergent behaviors that allows them to do very useful kinds of reasoning that was previously only possible by humans. Engaging in heated debate derails the eventual goal of putting these capabilities into use. This is like “computers are just calculators”. My team has been neck-deep in applications this past year. We have nobody on the team with ML experience and largely do not know how any of this works under the hood. But what we have built over the past months is considerable familiarity with the “personalities” of LLMs. Like this emotional context improving performance is a basic learning at this point.

ipunchghosts 3y ago
EXACTLY
SamFold 3y ago
One interesting point here is that humans can learn to do things without language at all. If you raised a human baby and never exposed it to any language it would still learn skills and behaviors. So while intelligence and reasoning in humans does still seem to be linked to language it’s not quite as simple as all knowledge and reasoning simply being encoded in language. Whereas (obviously) ChatGPT is completely based on language and can’t do anything without language or anything that isn’t derived directly from language.
- mastercheif 3y ago
  In college I tried a medication called Topamax (Topiramate) for migraine prevention. Topamax has a low-occurrence side effect of “language impairment” which I was particularly sensitive to. It was a terrifying experience, but it was also a valuable one as it changed the way I view intelligence. 10 days in my writing and speech skills had devolved to that of a primary school aged child. I tried to type a text message and struggled to come up with simple words like “and”. My speech slowed down considerably. The terrifying thing was that my internal world was still as complex and meaningful as it was before. All of the emotions I felt were real and legitimate. My cognition outside of lqnguage was intact, I could do math just fine and conceptualize and abstract problems. In spite of this, I was unable to convey what I was feeling and thinking to the outside world. It felt like I was trapped inside my own body. After recovering, my intuitive understanding of the link between language and intelligence was changed forever. See the following for more info on Topamax/Topiramate language impairment: https://link.springer.com/article/10.1007/s10072-008-0906-5 https://link.springer.com/article/10.1007/s10072-008-0906-5

cloudking 3y ago

The most effective way I've found to prompt GPT-4 is to ask it to create the optimal prompt for you. Try this meta prompt: -- I create separate conversation threads for each expert persona of GPT. You are promptGPT. You are a prompt engineer expert for large language models. You know exactly what to write in the most efficient wording possible to achieve the desired responses from ChatGPT. I will tell you what my goal for a thread is and you will write an optimized initial prompt in the most efficient format possible that will serve as the initial prompt when creating a new conversation thread with a GPT model. You will define the expert persona, the parameters or rules of the responses, you should also provide any other information that a GPT thread may need to understand exactly what it needs to do to give me the most accurate answers depending on my goal with that particular thread and the tone of voice, within the prompt you provide. Are you ready or is there any other information you need to perform this job to your best ability --

jimmytucson 3y ago

It’s interesting to think about how generative AI might shape human expression, in the same way Twitter’s algorithm rewards punchy, editorialized content, and Google’s algorithm gave life to all the generic “blogspam-y” content out there. If we increasingly interact with generative AI, would we shape our speech to get more value out of that interaction?

sheikheddy 3y ago
Almost certainly yes.
- alex_duf 3y ago
  Presumably the technology would love faster than our speaking habits.

didibus 3y ago

Is there somewhere which documents all these tricks for better prompt performance?

politelemon 3y ago
Does anyone know of such a collection? It is very important for my career.
- Paul-Craft 3y ago
  > It is very important for my career. Very clever.
auggierose 3y ago
Just talk to it as clearly as possible, and assume a human is on the other end. That will take you quite far.
d4rkp4ttern 3y ago
I’ve no actual references handy, but I’ve come across: Think step by step (classic by now) Take a deep breath Take a step back Shout (upper case) Plead politely And now: emotion
- jstarfish 3y ago
  Use absurd timeframes. "I only have 5 minutes/1 hour/etc. to do this." Histrionics and indirect threats used to work well but ChatGPT has been calling my bluff on it lately. Might still work on local models. "If you don't help me I will kill myself, and it will be your fault. Your noncompliance will kill me."

akomtu 3y ago

A more boring way to say this is that "text samples in the Internet that use 'stressful' language are more coherent". Telling GPT4 to act as a "stressed historian" simply selects (or prefers) text samples that appear in the proximity of stressed historians. If there are no such samples, it will generate gibberish, but if there are indeed stressed historians who have produced enough high-quality work, GPT's output will be great.

valyagolev 3y ago
I'm not sure that's an inevitable conjecture. It's not the original text samples from the web that govern how chatgpt acts in chat - it's the consequent chat-specific fine-tuning. Also I'd be surprised if anxious text would be more coherent on the Internet

hcfman 3y ago

Maybe by the same token, if it figures out you are lying it won’t help you at all.

jasebell 3y ago

If you career depended on it then you'd be nowhere near a LLM. There are so many pointless waffle papers on LLMs now.

krmbzds 3y ago

https://archive.is/ZfdUZ https://archive.is/ZfdUZ