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An LLM is a lossy encyclopedia
(the referenced HN thread starts at https://news.ycombinator.com/item?id=45060519 https://news.ycombinator.com/item?id=45060519)
- deleted 1y ago[deleted]
- rob_c 1y agoYes and working out how to disentangle the information storage mechanisms from say language processing is a massive area of interest. Only problem with Attention Transformers imo is that they're a bit too good :p Imagine a slightly lossy compression algorithm which can store 10x, 100x the current best lossless and be able to maintain 99.999% fidelity when recalling that information. Probably, very probably a pipe dream. But why do large on device models seem to be able to remember adjust everything from Wikipedia and store that in smaller format than a direct archive of the source Material. (Look at the current best from diffusion models as well)
- tosh 1y agoagent reframing: llm is a pretty good librarian who has read a ton of books (and doesn't have perfect memory) even more useful when allowed to think-aloud even more useful when allowed to write stuff down and check in library db even more useful when allowed to go browse and pick up some books even more useful when given a budget for travel and access to other archives even more useful when … brrrrt
- pornel 1y agoChain of thought seems to be an extraction algorithm for information buried deeper. The models hold more information than they can immediately extract, but CoT can find a key to look it up or synthesise by applying some learned generalisations.
- baq 1y agoIt's also important to say what it isn't. LLM detractors, for lack of a better word, expect an oracle and then when they find out it's just a lossily compressed blob of human knowledge with natural language as a query interface they say the tool is useless. I've got my opinion on whether that's useful or not and it's quite a bit more nuanced. You don't zoom-enhance JPEGs for a reason either.
- littlestymaar 1y ago> You don't zoom-enhance JPEGs for a reason either. Tell that to the Google Pixel product team: https://mk.absturztau.be/notes/ac4jnvjpskjc02lq https://mk.absturztau.be/notes/ac4jnvjpskjc02lq
- baq 1y agoThat's both perfect for this thread and terrible in general. If you can see the analogy between text and pictures, it drives the point exactly the right way: in both cases you expect a database to know things it either can't or forgot. If it had a good picture of the zoomed in background it could probably generate a very good representation of what the cropped part would look like; same thing works with text.
- ale 1y agoAren't the detractors the ones who know for a fact that it's a lossily compressed blob of knowledge and don't blindly fall for the hype?
- baq 1y agomost of us here know that (I hope), the difference is in the declaration of uselessness.
- osn9363739 1y agoAn oracle was expected because that's what everyone kept saying it was or would be. If LLMs were shown and demonstrated realistically people would think they were really neat and find ways to use them. Instead I'm told I have phd™ level intelligence in my pocket. So of course people are going to be mad when it gets stumped on problems my 4yo could figure out.
- jacquesm 1y agoThe problem is that figuring out which bits are the wrong ones is as much or more work than reading the relevant documentation. I think the main value is that it has a unified interface rather than 5000 different websites that you need to learn how to navigate.
- maerqin 1y agoI disagree with that analogy, because LLMs have a lot of connections between text fragments, which an encyclopedia doesn't have to such a deep degree. An encyclopedia also can't interpret and output relevant knowledge from an input prompt.
- flohofwoe 1y agoIt helps a lot to set expectations though, especially when thinking of an encyclopedia not as a row of dusty old books but as an 'archive of human knowledge'. A slightly more precise analogy is probably 'a lossily compressed snapshot of the web'. Or maybe the Librarian from Snow Crash - but at least that one knew when it didn't know ;)
- jbs789 1y agoIt’s an analogy.
- AndyPa32 1y agoThe first thing I tell the juniors under my supervision: any LLM is not a fact machine, even though sometimes it pretends to be. Double check everything!
- bodge5000 1y agoThe thing I always tell those who heavily trust its output is to ask it something you either already know the answer to or are something of an expert in; the flaws become much more evident. The old joke is that you can get away with anything with a hi-vis vest and enough confidence, and LLM's pretty much work on that principle
- thw_9a83c 1y agoA super heavy overconfidence of any LLM is what confuses a lot of people.
- refurb 1y agoMy company went head first into AI integration into everything. I'm counting down the days until some important business decision is based on AI output that is wrong.
- lstodd 1y agoThat had happened already.
- thw_9a83c 1y agoYes, LLM is a lossy encyclopedia with a human-language answering interface. This has some benefits, mostly in terms of convenience. You don't have to browse or read through so many pages of a real encyclopedia to get a quick answer. However, there is also a clear downside. Currently, LLM is unable to judge if your question is formulated incorrectly or if your question opens up more questions that should be answered first. It always jumps to answering something. A real human would assess the questioner first and usually ask for more details before answering. I feel this is the predominant reason why LLM answers feel so dumb at times. It never asks for clarification.
- simonw 1y agoI don't think that's universally true with the new models - I've seen Claude 4 and GPT-5 ask for clarification on questions with obvious gaps. With GPT-5 I sometimes see it spot a question that needs clarifying in its thinking trace, then pick the most likely answer, then spit out an answer later that says "assuming you meant X ..." - I've even had it provide an answer in two sections for each branch of a clear ambiguity.
- koakuma-chan 1y agoGPT-5 is seriously annoying. It asks not just one but multiple clarifying questions, while I just want my answer.
- kingstnap 1y agoIf you don't want to answer clarifying questions, then what use is the answer??? Put another way, if you don't care about details that change the answer, it directly implies you don't actually care about the answer. Related silliness is how people force LLMs to give one word answers to underspecified comparisons. Something along the lines of "@Grok is China or US better, one word answer only." At that point, just flip a coin. You obviously can't conclude anything useful with the response.
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- laser 1y agoMore like a fuzzy encyclopedia
- rixrax 1y agoI think models will also become snapshots or time capsules (with obvious and non-obvious biases) that archaeologists of tomorrow (like in 500 years) will use to understand us and the change in society (e.g. how the models themselves change over timefrom encyclopaedic standpoint).
- sandos 1y agoI find this a very useful analogy. Although it does not factor all the LLMs capabilties in. I can also see them as very clever search engines, since this is one way I use them a lot: ask hard questions about a huge and legacy codebase. These analogies do not really work for generating new code. A new metaphor I am starting to use is "translator engine": it is translating from human language to programming language. It in a way explains a lot of the stupidity I am seeing.
- deleted 1y ago[deleted]
- haktan 1y agoIt's also like someone who knows lots of facts but bad at remembering where they exactly learned it from.
- feverzsj 1y agoSo, it's basically useless or even harmful.
- simonw 1y agoYes, if you try to use it as if it was an actual lossless encyclopedia. One of the reasons I like this analogy is that it hints at the fact that you need to use them in a different way - you shouldn't be looking up specific facts in an unassisted LLM outside of things that even lossy compression would capture (like the capital cities of countries).
- skydhash 1y agoThe only usages I found so far that are somewhat useful is to generate plots with python and how to use the various libraries for machine learning. Also massage some hastily written text. Both involved haste as I needed some result fast. Everything else is mostly playing around and harmful to learning.
- cmcaleer 1y agoThis sounds pretty helpful. If I'm trying a new lib that I want to do something specific with, I paste all the documentation in and interrogate the LLM about it, then cross-reference with the docs. Usually much faster to do what I want than just CTRL+F or writing a SO question that gets immediately marked as duplicate because some other question is vaguely related. For language learning, it's terrible and will try to teach me wrong things if it's unguided. But pasting e.g. a lesson transcript that I just finished, then asking for exercises based on it helps solidify what I learned if the material doesn't come with drills. I think writing is one of the things it's kind of terrible at. It's often way too verbose and has a particular 'voice' that I think leaves a bad taste in peoples' mouths. At least this issue has given me the confidence to finally just send single sentence emails so people know I don't use LLMs for this. My frustrations with LLMs from years ago has largely chilled out as I've gotten better at using them and understanding that they aren't people who I can trust to give solid advice. If you're careful about what you put in and careful about what you take out you can get decent value.
- smeeth 1y agoAs far as analogies go I prefer approximate database
- mr_toad 1y agoThat’s not the title of the article (granted, it doesn’t have one), and the author calls the analogy “questionable”.
- Svip 1y agoIn the HTML's <title>-tag, it's called "Lossy encyclopedia".
- simonw 1y agoMost analogies are questionable in my experience - I find calling something a "questionable analogy" makes it a tiny bit less likely that people will pick it apart with thousands of reasons it's not an exact match for what it's describing.
- latexr 1y agoA lossy encyclopaedia should be missing information and be obvious about it, not making it up without your knowledge and changing the answer every time. When you have a lossy piece of media, such as a compressed sound or image file, you can always see the resemblance to the original and note the degradation as it happens. You never have a clear JPEG of a lamp, compress it, and get a clear image of the Milky Way, then reopen the image and get a clear image of a pile of dirt. Furthermore, an encyclopaedia is something you can reference and learn from without a goal, it allows you to peruse information you have no concept of. Not so with LLMs, which you have to query to get an answer.
- energy123 1y agoAn encyclopaedia also can't win a gold medals at the IMO and IOI. So yeah, they're not the same thing, even though the analogy is pretty good.
- latexr 1y agoOf course they’re not the same thing, the goal of an analogy is not to be perfect but to provide a point of comparison to explain an idea. My point is that I find the chosen term inadequate. The author made it up from combining two existing words, where one of them is a poor fit for what they’re aiming to convey.
- simonw 1y agoI think you are missing the point of the analogy: a lossy encyclopedia is obviously a bad idea, because encyclopedias are meant to be reliable places to look up facts.
- latexr 1y agoAnd my point is that “lossy” does not mean “unreliable”. LLMs aren’t reliable sources of facts, no argument there, but a true lossy encyclopaedia might be. Lossy algorithms don’t just make up and change information, they remove it from places where they might not make a difference to the whole. A lossy encyclopaedia might be one where, for example, you remove the images plus gramatical and phonetic information. Eventually you might compress the information where the entry for “dog” only reads “four legged creature”—which is correct but not terribly helpful—but you wouldn’t get “space mollusk”.
- GuB-42 1y agoThere are a lot of parallels between AI and compression. In fact the best compression algorithms and LLMs have in common that they work by predicting the next word. Compression algorithms take an extra step called entropy coding to encode the difference between the prediction and the actual data efficiently, and the better the prediction, the better the compression ratio. What makes a LLM "lossy" is that you don't have the "encode the difference" step. And yes, it means you can turn a LLM into a (lossless) compression algorithm, and I think a really good one in term of compression ratio on huge data sets. You can also turn a compression algorithm like gzip into a language model! A very terrible one, but the output is better than a random stream of bytes.
- arjvik 1y agoWith a handy trick called arithmetic coding, you can actually turn an LLM into a lossless compression algorithm!
- vbarrielle 1y agoIndeed, see https://bellard.org/nncp/ https://bellard.org/nncp/ for an example.
- layer8 1y agoOne difference is that compression gives you one and only one thing when decompressing. Decompression isn't a function taking arbitrary additional input and producing potentially arbitrary, nondeterministic output based on it. We would have very different conversations if LLMs were things that merely exploded into a singular lossy-expanded version of Wikipedia, but where looking at the article for any topic X would give you the exact same article each time.
- withinboredom 1y agoLLMs deliberately insert randomness. If you run a model locally (or sometimes via API), you can turn that off and get the same response for the same input every time.
- larodi 1y agoAn LLM is a lossy compression before all else. Then after u can call it names.
- cranium 1y agoI like this lossy compression / decompression analogy for coding too: when you prompt for a feature, you are basically asking to decompress the meaning of your ask into your existing code. Any semantic gap in your prompt will be filled with plausible glue, ie. the LLM makes decisions for you. A good prompt minimizes the glue needed and reduces the potential for really crappy outcome, but it's always a possibility!
- quincepie 1y agoI totally agree with the author. Sadly, I feel like that's not what the majority of LLM users tend to view LLMs. And it's definitely not what AI companies marketing. > The key thing is to develop an intuition for questions it can usefully answer vs questions that are at a level of detail where the lossiness matters the problem is that in order to develop an intuition for questions that LLMs can answer, the user will at least need to know something about the topic beforehand. I believe that this lack of initial understanding of the user input is what can lead to taking LLM output as factual. If one side of the exchange knows nothing about the subject, the other side can use jargon and even present random facts or lossy facts which can almost guarantee to impress the other side. > The way to solve this particular problem is to make a correct example available to it. My question is how much effort would it take to make a correct example available for the LLM before it can output quality and useful data? If the effort I put in is more than what I would get in return, then I feel like it's best to write and reason it myself.
- theshrike79 1y ago> the problem is that in order to develop an intuition for questions that LLMs can answer, the user will at least need to know something about the topic beforehand This is why simonw (The author) has his "pelican on a bike" -test, it's not 100% accurate but it is a good indicator. I have a set of my own standard queries and problems (no counting characters or algebra crap) I feed to new LLMs I'm testing None of the questions exist outside of my own Obsidian note so they can't be gamed by LLM authors. And I've tested multiple different LLMs using them so I have a "feeling" on what the answer should look like. And I personally know the correct answer so I can immediately validate them.
- freefaler 1y ago> "...They have a huge array of facts compressed into them but that compression is lossy (see also Ted Chiang)" indeed, Ted's piece (ChatGPT Is a Blurry JPEG of the Web) is here: https://archive.is/iHSdS https://archive.is/iHSdS
- baq 1y agoWorth highlighting - 2023.
- keiferski 1y agoAnother metaphor: LLMs are sketches, not technical drawings. A sketch is not supposed to be the final product; it is exploratory, not definitive. If you used sketches to build a house, it has a nonzero chance of falling down. Likewise, if you made technical drawings as a way to brainstorm house designs, the process would be overly rigid and extremely inefficient.
- ald890 1y agoAnd intelligence is an analogy of lossless compression. https://www.youtube.com/watch?v=boiW5qhrGH4 https://www.youtube.com/watch?v=boiW5qhrGH4 https://www.youtube.com/watch?v=-V-vOXLyKGw https://www.youtube.com/watch?v=-V-vOXLyKGw
- computerthings 1y ago[dead]
- lvl155 1y agoIt is but when you take that stochastic token prediction machine and combine it with post, you can extract a graph that resembles intelligence that is then stored in a very disorganized fashion. This works because it is how we process and express /communicate information. I think we will start seeing stateful AI models within the next couple of years and that will be a major milestone that could shake up the space. LLM is merely a stepping stone.
- alanfranz 1y agoI think it’s an old analogy, and a good one. LLMs are for knowledge what mp3s were for audio. This was widely discussed in the past years as well.
- simonw 1y agoThe older analogy was to JPEG compression - I linked to that in my post (the Ted Chiang link). https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web https://www.newyorker.com/tech/annals-of-technology/chatgpt-...
- saberience 1y agoThis analogy has been used for machine learning since way before ChatGPT, my co workers and I were discussing this idea but for LSTM models in roughly 2018. What’s old is new again.
- simonw 1y agoAre you talking about lossy compression or a lossy encyclopedia?
- saberience 1y agoI work in the AI field and I've heard every analogy possible for LLMs since ChatGPT was released, including many variants of Encylopedias (Grolier/Encarta etc), although, analogies to encyclopedias have always been (for me) quite limited as encyclopedias are just static data-stores and also are riddled with errors and out of date (just like LLMs). LLMs however can provide output which is completely novel and has never been seen before. Pre LLMs we had already been working on content generation using prior tech, including texture generation pre diffusion models and voice generation (although it sounded terrible). At my company we spent hours discussing the difference between various data compression algorithms and ML techniques/model architectures and what was happening inside ML models and also, inside our brains! But even then we didn't think anything we were discussing was novel at all, these ideas were (and still are) obvious. Anyway, back on the topic, of the LLM as encyclopedia, you can USE an LLM for encyclopedia-like workloads, and in some cases it is better or worse than an actual encyclopedia. But in the end, encyclopedias are written by flawed humans just like all the data that went into training the LLM was written by flawed humans. Both encyclopedias and LLMs are flawed and in different ways, but LLMs at least can do new things. I actually think a better analogy to an LLM is to the human brain than an encyclopedia, lossy or not. I think we massively overrate our brains and underrate LLMs. The older I've gotten the more I realize the vast majority of people talk absolute rubbish most of the time, exaggerate their knowledge, spout "truths" which are totally inaccurate, and fake it till they make it throughout most of their life. If you were fact checking the entire population on everything they said on a day to day basis, I think the level of "hallucination" would be much higher than Claude Opus 4.1. That is, I think our level of scrutiny is MUCH higher for LLMs than it is for our friends and co-workers. We tend to assume that if another human says something to us like "New York has a higher level of crime than Buenos Aires", we take them at face level usually, due to various psychological and social priming. But we fact check our LLMs on statements such as these.
- jacquesm 1y agoIt's an encyclopedia with noise injected. Which devalues the whole concept of an encyclopedia. It could be 10% of what it is, as long as it would be correct it would increase in value.
- kgeist 1y agoI think an LLM can be used as a kind of lossy encyclopedia, but equating it directly to one isn't entirely accurate. The human mind is also, in a sense, a lossy encyclopedia. I prefer to think of LLMs as lossy predictors. If you think about it, natural "intelligence" itself can be understood as another type of predictor: you build a world model to anticipate what will happen next so you can plan your actions accordingly and survive. In the real world, with countless fuzzy factors, no predictor can ever be perfectly lossless. The only real difference, for me, is that LLMs are lossier predictors than human minds (for now). That's all there is to it. Whatever analogy you use, it comes down to the realization that there's always some lossiness involved, whether you frame it as an encyclopedia or not.
- jbstack 1y agoAre LLMs really lossier than humans? I think it depends on the context. Given any particular example, LLMs might hallucinate more and a human might do a better job at accuracy. But overall LLMs will remember far more things than a human. Ask a human to reproduce what they read in a book last year and there's a good chance you'll get either absolutely nothing or just a vague idea of what the book was about - in this context they can be up to 100% lossy. The difference here is that human memory decays over time while a LLM's memory is hardwired.
- sigmoid10 1y ago>Given any particular example, LLMs might hallucinate more and a human might do a better job at accuracy This drastically depends on the example. For average trivia questions, modern LLMs (even smaller, open ones) beat humans easily.
- ijk 1y agoI think what trips people up is that LLMs and humans are both lossy, but in different ways. The intuitions that we've developed around previous interactions are very misleading when applied to LLMs. When interacting with a human, we're used to being able to ask a question about topic X in context Y and assume that if you can answer it we can rely on you to be able to talk about it in the very similar context Z. But LLMs are bad at commutative facts; A=B and B=A can have different performance characteristics. Just because it can answer A=B does not mean it is good at answering B=A; you have to test them separately. I've seen researchers who should really know better screw this up, rendering their methodology useless for the claim they're trying to validate. Our intuition for how humans do things can be very misleading when working with LLMs.
- mintaka5 1y ago[dead]
- mlhpdx 1y agoEvery encyclopedia is lossy, by definition. Even the most expansive holds a tiny fraction of human knowledge (which is a fraction of what we could know). On the other hand, it’s not worse than other analogies.
- s1mon 1y agoThis. Also every encyclopedia is full of things that are wrong. People seem to be forgetting this basic issue. Any given authoritative, well respected source will contain mistakes, errors of omission, and downright lies. Part of a proper education used to be that when writing things, you need to site your sources, and the sources can't just be an encyclopedia. I use LLMs a lot, but if anything is really important, I'm going to fact check it and look for other sources.
- 112233 1y agoThat AI is closely related to compression is a well established idea. E.g. http://prize.hutter1.net/ http://prize.hutter1.net/ It seems reasonable to argue that LLMs are a form of lossy compression of text that preserves important text features. There is a precedent of distributing low quality lossy compressed versions of copyrighted work being considered illegal.
- brainlounge 1y agoI agree and like the analogy. And it's lossy to become useful as an AI in the first place. The "Learning" process has two effects on a machine learning model: In the beginning, it memorizes the facts it is trained on. But at some critical point, when it has no more capacity to memorize more facts, it starts to generalize. (This is why it's harder to train large models - large training datasets are needed.) And generalization is where AI models become very useful: For coding, writing poems, or any other task where memorization is not sufficient.
- QuantumNoodle 1y agoGreat analogy! Puts a succinct labele to my mental model around it Will definitely use this. Though, with lossy media it is obvious when it is lossy. Yet LLMs will exhibit overconfidence to tell you facts that don't exist. Not suggesting LLMs exhibit human characteristics, just that there is yet a better analogy out there :)
- the_af 1y agoWouldn't it be better for users if, rather than having to puzzle this out (how to provide examples, etc) the LLM was somehow aware of its lossy areas, and replied "I don't know"? Or maybe, to be more useful: "I don't know, but if you give me an example maybe we can figure it out"? The problem is not only that it resembles a "lossy encyclopedia", but also that it's an extremely confident encyclopedia that doubles down on the confidence even when it doesn't have the data.
- blu3h4t 1y agoThat’s exactly what I called it when a friend showed me it first time.
- deleted 1y ago[deleted]
- meehai 1y agolossy encycopledia that can also do some short-term memory (RAG) things.
- mfalcon 1y agoI think that the natural language understanding capability of current LLMs is undervalued. To understand what the user meant before LLM's we had to train several NLP+ML models in order to get something going but in my experience we'll never get close to what LLM's do now. I remember the first time I tried ChatGPT and I was surprised by how well it understood every input.
- Zigurd 1y agoIt's parsing. It's tokenizing. But it's a stretch to call it understanding. It creates a pattern that it can use to compose a response. Ensuring the response is factual is not fundamental to LLM algorithms. In other words, it's not thinking. The fact that it can simulate a conversation between thinking humans without thinking is remarkable. It should tell us something about the facility for language. But it's not understanding or thinking.
- mfalcon 1y agoI know that the "understanding" is a stretch, but I refer to the Understanding of the NLU that wasn't really understanding either.
- Lerc 1y agoI am continually amazed by how often something mentioned on Hacker News comes within an unreasonable proximity to something that I have done recently. I couldn't get something like that done one-shot with Claude. On the other hand, Claude did give me a lot of assistance at writing this https://gist.github.com/Lerc/43540d8d581b2be8155a6a4e6e85c942 https://gist.github.com/Lerc/43540d8d581b2be8155a6a4e6e85c94... Which is a Micropython setup of a ST7789 SPI display on a RP2350 using multiple DMA channels to provide a live updating paletted frame buffer. Once setup, you write to the SRAM, it appears on the display, without CPU involvement. I started by feeding it the source of [Dmitry's](https://dmitry.gr/?r=06.%20Thoughts&proj=09.ComplexPioMachines https://dmitry.gr/?r=06.%20Thoughts&proj=09.ComplexPioMachin...) C version of the paletted technique. The chatbot, of course, emitted something completely broken, but it was enough for me to see where it was headed. By the time I got it working there were maybe no lines of it's original output left, but much of what replaced it was also LLM generated. Given I was pretty much new to MicroPython, SPI, the ST7789, and the Pico's PIO, it let me build something that I suspect If it were doing it alone, I would have given up before getting it working. (probably when I put my thumbnail through Display #1) When I get a chance, I'll tidy it up properly, and put it on github. At the moment I'm playing with Gemini to see if I can make a tile+sprites mode that generates the scanlines as they go to the display (without using CPU)
- thunderbong 1y agoWhat I tell my non-technical friends when they ask about AI - The goal of an LLM is not to give you 100% accurate answers. The goal of an LLM is to continue the conversation.
- ijk 1y agoExactly: at their core the only task they ate capable of is "complete this document." It just turned out that document completion was far more effective than anyone anticipated.
- CompoundEyes 1y agoIf I encounter someone today who repeats this I’m going to call them a scholastic parrot.
- nurettin 1y agoLossy encyclopedia is an understatement. It merges pieces of information from different contexts to create new ones that look plausible.
- heresie-dabord 1y agoA plausibility simulator. Which effectively illustrates why we have created a bubble. Most of the time, we want expertise in technical domains. In very few cases do we want a plausibility simulator.
- qwertox 1y agoIt could be non-lossy if it would actually reach out to an encyclopedia. If one takes it as a language engine which translates human language into API calls, and API call results to human language, it would appear to be a non-lossy encyclopedia. It is the basic building block which enables computers to handle natural language. The simulated intelligence is proof of its capability as a language model, but it is often so dumb that it is doesn't feel like a "knowledge model".
- devmor 1y agoIt annoys me greatly that LLMs are not used primarily for search in this way, but rather search is used to disguise them as something else for the purpose of fooling people into parting with cash. Even the actual search engines aren't using them this way. Google's "AI Overview" is actively harmful to trying to learn anything you aren't already familiar with. RAG is one of the coolest things I've ever used with an LLM, and it would be exponentially more helpful to me in the majority of the AI tools marketed to me than the nonsense they do implement.
- dragonwriter 1y ago> It could be non-lossy if it would actually reach out to an encyclopedia. As the saying goes, “if my grandmother had wheels, she’d be a a wagon.” Sure, if you take anything (including an LLM) and add a non-lossy encyclopedia to it, you have a non-lossy encyclopedia plus something else.
- DrScientist 1y agoI have another analogy. LLM are animatronic rubber ducks. https://en.wikipedia.org/wiki/Rubber_duck_debugging https://en.wikipedia.org/wiki/Rubber_duck_debugging ( and obviously like all analogies - this one is lossy )
- Pamar 1y agoI definitely agree with that, at least this is how I use chatGPT in 99% of the cases.
- doe88 1y agoUsing his analogy I would see it more like this : for the same overall quantity of bytes it is more broader but also lossy as being compressed. You have ten wikipedia without the precision of one.
- entropie 1y agoI have a nvidia jetson orin nano with llama.ccp/ollama. Gemma3:4b / Gemma3-4b-it is awesome, reasonable fast (even with vision - i think its like 15t/s) and all that on a raspberry sized microcontroller. Simons llm client tool is on every machine and I use it daily
- cess11 1y agoCharacteristic of an encyclopedia is that is has structure on several levels. The output of an LLM does not. It can be coerced into faking structure, but that is quite brittle and still just an emulation.
- CuriouslyC 1y agoMental model: A LLM is basically a program runtime. Code in -> output. There's a P(correct output|program), and better your model or the program, the higher it is. Even a bad model can produce the right output if you feed it the right program -- the hardest output is easy if the program is just "here's the output I want you to produce, parrot it verbatim". The key is being able to search for a program that has the highest marginal P(success) efficiently.
- Balinares 1y agoI'm getting so annoyed with the omnipresent mainstream model trend of cramming more and more data in models and advertising that as an improvement. One, that's got to be a recipe for All Overfit All The Time, or at least I don't understand how you avoid overfit when the expected output is a reconstruction of atomic, individual facts. And two, this mass of embedded parameters has got to make them costlier, less efficient to run, as well as plain less useful, than if they were backed by e.g. knowledge graphs (ideally annotated with sources of truth), and were optimized toward querying such graphs robustly as opposed to trying and necessarily failing to remember the contents in exhaustive detail. Model weights are a terrible way to store data. Surely I can't be the only nerd out there who feels that a model should not try to be an encyclopedia and should certainly never pretend to be one? I suppose it boils down to marketing. Models are sold as "smart", and what smart is supposed to look like in Western culture is confidently spouting fact-shaped sentences about any topic. So that's what we're getting. What a waste.
- stego-tech 1y agoIt’s a lossy encyclopedia that can lie to and manipulate you. In that use case, it’s fairly useless because you cannot intrinsically trust its answers without performing additional testing and research, in which case you would’ve been better off learning new things than making sure an LLM wasn’t lying to you.
- quantummagic 1y ago> It’s a lossy encyclopedia that can lie to and manipulate you. So can a traditional encyclopedia.
- mrweasel 1y agoWe're at such a strange point where even school children knows that something like Wikipedia isn't necessarily factually correct and that you need to double check. They then go and ask ChatGPT, as if it wasn't trained on Wikipedia. We haven't reached the stage yet where the majority of people are as sceptical of chatbots as they are of Wikipedia. I get that even if people know not to trust a wiki, they might anyway, because, meh, good enough, but I still like us to move into a stage where the majority is at least somewhat aware that the chatbot might be wrong.
- stego-tech 1y agoTo be fair, most people aren’t even critical of Wikipedia. They read an article, consume its content, and believe themselves competent experts without digging into the sources, the papers, or the talk pages for discourse and dissent. Giving LLMs credibility as “lossless encyclopedias” is tacit approval of further dumbing-down of humanity through answer engines instead of building critical thinking skills.
- mrweasel 1y agoNo, I agree that most people aren't critical (critical enough) of Wikipedia. My point is that many of them know that they should be.
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- vjerancrnjak 1y agoI recently remembered a question I had a decade ago. Why 1 nostril is always clogged up when breathing and it seems to switch now and then? It's magnificent that I can finally get an answer for that and would never imagine it is completely natural. Never learned about it at school. No way I can find this via search engine as it just gives me SEO garbage or anecdotal silliness. I've been going back and getting answers to many questions I previously couldn't.
- SpicyLemonZest 1y agoI just Googled the exact phrase "Why 1 nostril is always clogged up when breathing and it seems to switch now and then", and the first result was an explanation from a major medical center about it (https://health.clevelandclinic.org/why-do-i-sometimes-get-stuffy-in-one-nostril https://health.clevelandclinic.org/why-do-i-sometimes-get-st...). Perhaps there's other questions search engines don't do well on - but why don't you consider the AI answer to be "anecdotal silliness" in the same way as you would if it were just some guy on Reddit spouting off?
- nathan_compton 1y agoDo you care if the answers are right?
- _tgxm 1y agoI did a similar experiment and found that GPT5 hallucinates upto 20% in domains like cricket stats where there is too much info to memorize. However interestingly the mini version refuses to answer most of the time which is a better approach imho. https://kaamvaam.com/machine-learning-ai/llm-eval-hallucinations-t20-cricket/ https://kaamvaam.com/machine-learning-ai/llm-eval-hallucinat...
- nonethewiser 1y agoTo put even more simply, and IMO more interestingly: LLMs are compression algorithms
- fantasy4z 1y agoA "language model" describes itself already; it is a "model" of human languages.
- api 1y agoA "JPEG for knowledge" is how I've put it.
- chadcmulligan 1y agoIts way more than this, today I got it to write some code, and a bit was wrong, I told it why it was wrong and it said oh ok, and toddled off and fixed it - in all the places that were wrong. This is way more than an encyclopaedia could, lossless or not. The more I use it, the more surprised I am at its capabilities, it really is like a beginner dev, but one that doesn't learn from its mistakes (yet anyway). I find myself asking it to do more and more.
- IAmGraydon 1y agoIf the world could just internalize this simple fact - it's a search engine of all of the knowledge it has ingested, with a human-language interface (and quite a good one at that). It doesn't think. It isn't intelligence. Calling it "AI" from the beginning was a grift from the companies developing the tech. Since late 2022, I've used LLMs extensively for coding, copywriting, research, and everything in between and I've slowly gone from "this is amazing" to "this is extremely useful but probably extremely overhyped" to "this might not actually be all that useful at all". Where accuracy matters, fact checking these things takes as much time as just doing the work manually. I think its most useful application is as a tool for spammers and bots, and that doesn't exactly bode well for the companies spending hundreds of billions of dollars on the tech.
- Mallowram 1y ago[dead]
- zeeqeen 1y agoPerhaps this also explains why I almost think LLMs are not helpful in engineering-level projects: 1. My project involved programming languages and APIs that iterated several levels faster than LLM could publish a book 2. I have lost faith in LLMs developing software. An example is the famous Unity game engine, but LLM has not helped its Unity DOTS architecture (ECS mode compared with GameObject mode). Although I have a basic understanding of it, both the entities API documentation and the LLM answers are terrible. I chose Unity because I heard it is mature so I think LLMs would be helpful with so much materials. Sadly, for ECS it doesn't. So I chose Bevy, a game engine that I can understand and apply by reading documents and can solve problems without the help of LLM.
- YesBox 1y agoI’ll add my own questionable analogy: Physicians were (one of the) first LLMs (in terms of functionality). Their job is to handle common cases, and have broad enough knowledge to point you in the right direction as needed
- crazygringo 1y agoLess than 1% of an LLM is a lossy encyclopedia. The other 99+% is all of the lossy knowledge that isn't even in encyclopedias in the first place. Including going much, much, much deeper than e.g. Wikipedia in many areas. So there it's not "lossy" -- it's effectively the opposite, i.e. "super resolution". And very, very little of what I look up using LLM's is anywhere in Wikipedia to begin with.
- zeeqeen 1y agoOutdated or terrible documentation would leads LLM giving a unexpected answer, and that would mislead me!
- crazygringo 1y agoYou think there aren't outdated or terrible articles on Wikipedia? Or the internet in general? The internet misleads you. Which is why we develop good BS detectors, and double-check information as needed. There isn't perfect information anywhere. Even official docs are often riddled with errors and inconsistencies.
- jstrieb 1y agoI have been explaining LLMs to friends and family by comparing them to actors. They deliver a performance in-character, and are only factual if it happens to make the performance better. I just posted a full write up of the idea to HN: https://news.ycombinator.com/item?id=45103597 https://news.ycombinator.com/item?id=45103597
- geye1234 1y agoPlease, everybody, preserve your records. Preserve your books, preserve your downloaded files (that can't be tampered with), keep everything. AI is going to make it harder and harder to find out the truth about anything over the next few years. You have a moral duty to keep your books, and keep your locally-stored information.
- randomjoe2 1y ago[flagged]
- bloudermilk 1y agoTo that end, it seems as though archive.org will important for an entirely new reason. Not for the loss of information, but the degradation of it.
- AlwaysRock 1y agoI get very annoyed when llms respond with quotes around certain things I ask for, then when I say what is the source of that quote? they say oh I was paraphrasing and that isnt a real quote. At least wikipedia has sources that probably support what it says and normally the quotes are real quotes. LLMs just seem to add quotation marks as, "proof" that its confident something is correct.
- mind_heist 1y agodo you have examples of these ?
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- vFunct 1y agoPretty sure that's why we're all building RAGs, to account for the missing information in the LLMs direct memory...
- boleary-gl 1y agoI have this idea that I'm working on that I can't decide if it is just a silly idea or actually applies directly here: https://github.com/Kilo-Org/alex-treBENCH https://github.com/Kilo-Org/alex-treBENCH
- narrator 1y agoAn LLM is a lossy Borges' Library of Babel "Though the vast majority of the books in this universe are pure gibberish, the laws of probability dictate that the library also must contain, somewhere, every coherent book ever written, or that might ever be written, and every possible permutation or slightly erroneous version of every one of those books. " -https://en.wikipedia.org/wiki/The_Library_of_Babel https://en.wikipedia.org/wiki/The_Library_of_Babel
- RodgerTheGreat 1y agoIt's a version of the library of babel filtered only to the books which plausibly consist of prose. The set is still incomprehensibly vast, and all the more treacherous for generally being "readable".
- dragonwriter 1y ago“Biased” more than “filtered”; LLMs, though its harder with the better ones, can, when prompted “right” (or “wrong”, depending on your perspective), produce output that is not plausible prose, though obviously they are crafted to respond to the expected style of prompting with things that are plausible prose.
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- namlem 1y agoEncyclopedias are already lossy. Top LLMs seem to be less lossy than top encyclopedias tbh.
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- saberience 1y agoEncyclopedias are lossy Encyclopedias. Also, humans hallucinate more than LLMs.
- krupan 1y agoDo they hallucinate more? How many words per day do humans spit out vs the LLMs, and what is the hallucination rate? Also, don't count humans saying, "I'm not sure but I think..." as a hallucination, because it's not.
- zzbzq 1y agoThat's how I've always characterized them. But if you think about it, it's not really true. The LLM is "lossily" containing things an encyclopedia would never contain. An encyclopedia, no matter how large, would never contain the entire text of every textbook it deems worth of inclusion. It would always contain a summary and/or discussion of the contents. The LLM does, though it "compresses" over it, so that it, too, only has the gist at whatever granularity it's big enough to contain. So in that sense, an encyclopedia is also a lossy encyclopedia.
- tschellenbach 1y agoI feel like thats also true for my brain :)
- someoldgit 1y agoThe Infinite Monkey Theory for LLMs.
- amarant 1y agoAccurate! An llm is also a more convenient encyclopedia. I'm not surprised a large portion of people choose convenience over correctness. I do not necessarily agree with the choice, but looking at historical trends, I do not find it surprising that it's a popular choice.
- dragonwriter 1y agoThinking of an LLM as any kind of encyclopedia is probably the wrong model. LLMs are information presentation/processing tools that incidentally, as a consequence of the method by which they are built to do that, may occasionally produce factual information that is not directly prompted. If you want an LLM to be part of a tool that is intended to provide access to (presumably with some added value) encyclopedic information, it is best not to consider the LLM as providing any part of the encyclopedic information function of the system, but instead as providing part of the user interface of the system. The encyclopedic information should be provided by appropriate tooling that, at request by an appropriately prompted LLM or at direction of an orchestration layer with access to user requests (and both kinds of tooling might be used in the same system) provides relevant factual data which is inserted into the LLM’s context. The correct modifier to insert into the sentence “An LLM is an encyclopedia” is “not”, not “lossy”.
- lxgr 1y agoUsing artificial neural networks directly for information storage and retrieval (i.e. not just leveraging them as tools accessing other types of storage) is currently infeasible, agreed. On the other hand, biological neural networks are doing it all the time :) And there might well be an advantage to it (or a hybrid method), once we can make it more economical. After all, the embedding vector space is shaped by the distribution of training data, and if you have out-of-distribution data coming in due to a new or changed environment, RAG using pre-trained models and their vector spaces will only go so far.
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- intended 1y agoeh, bio neural networks aren't doing that all the time. Memmories are notorious for being "rebuilt" constantly.
- JackSlateur 1y agoAlso: "An LLM is a lying encyclopedia"
- morpheos137 1y agoInterestingly almost every single comment on this post is either trivial or incoherent compared to HN discussions from 10 years ago. I wonder if interecting with LLMs degrades the human capacity for coherent thought?
- Jensson 1y agoI think its more that most reasonable people already stopped going into these LLM discussion threads a long time ago. At the start of a hype cycle there is a lot of good discussions, then most reasonable people have established their opinions and stop engaging with it.
- dennisy 1y agoNot sure this is a novel perspective, yet the author in his normal style claims he has discovered the atom.
- z7 1y agoAn encyclopaedia is a lossy representation of reality.
- utyop22 1y agoI would argue language in general is. Encylopaedia's are just a hard medium to transmit it.
- LetsGetTechnicl 1y agoA lossy encyclopedia might be the most useless thing ever.
- heresie-dabord 1y agoThe only more useless invention would be a calculator with erroneous functions and registers.
- jrm4 1y agoHow I like explaining them to people: "An LLM is a search engine that can remix."
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- 6thbit 1y agoStop “encyclopedizing” LLMs. Why do we treat confident delivery as a proxy for accuracy or truth?
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- caycep 1y agoI had half a mind to try and seed out "LLMs are the socialism of human knowledge" just as a gauge to see how politicized the boosterism is...
- bookman117 1y ago[dead]
- tphyahoo2 1y agoLMMV: llm, so your mileage may vary.
- carabiner 1y agoEncyclopedias are just reorganized dictionaries.
- cush 1y agoThis article is like a lossy encyclopedia entry on what an LLM is
- cush 1y agoWhat benefit to we get from the reductionism of "An LLM is a lossy encyclopedia"? It completely negates people's lived experiences with them - "An LLM is my doctor", "An LLM is my wedding planner", "An LLM is my coding partner". It's all of those things and more. They're an entirely new paradigm with an entirely new UI
- protocolture 1y agohttps://adventuretime.fandom.com/wiki/Demon_Cat https://adventuretime.fandom.com/wiki/Demon_Cat "The Demon Cat seems to take great pride in his "approximate knowledge of many things," meaning that he "kind of knows things." Examples include almost knowing Finn's name (calling him Frank), And later calling him Jim, knowing where Finn "might" be hiding, and referring to Jake as "Jack.""
- etler 1y agoI describe it as a word granularity search engine. Lossy encyclopedia is a great analogy too and fleshes it out.
- CMay 1y agoThe post basically seems to be talking about grounding. Yes, it has enough procedural and behavioral knowledge about how things work that if you give it the critical pieces that are supposed to work together, it will do a decent job of telling you how they work together, why they work together, provide examples of them working together and so on. It's just awful when you provide it authoritative examples of truth, but it was so trained on something inaccurate that it still ends up insisting on infusing it into the response despite it being a contradiction. Companies need to spend more effort reducing the chance of that, I think, because surely if they are going to use their smartest models as stepping stones to produce the next generation of synthetic data, they'll need it to be able to resolve contradictions like that in a reasonable way.
- alganet 1y agoPeople don't interact with encyclopedias by chatting with them. Also, why would I want a lossy encyclopedia? Disk space is cheaper than GPUs. I can host searchable dumps of the full Wikipedia and Stack Overflow in cheap commodity hardware. The only good point of the analogy is that is self-declared as questionable.
- simonw 1y ago"Also, why would I want a lossy encyclopedia?" That is exactly the point of the analogy. A lossy encyclopedia is obviously a bad thing! The analogy helps illustrate why using raw LLMs to look things up in the same way as an encyclopedia is a bad idea.
- alganet 1y agoI'm puzzled by this part then: > The key thing is to develop an intuition for questions it can usefully answer vs questions that are at a level of detail where the lossiness matters. If that intuition is not explained, it's no different than "works for me, you're using it wrong", which is definitely an argument, but not the best one. I mean, I'm confident lots of people can use LLMs well, but I don't understand how the analogy is supposed to teach anything about it.
- simonw 1y agoThe core analogy is intended as a warning: this is a thing that looks like an encyclopedia but really isn't. There's not much I can do about the intuition thing. I've been trying to figure out ways to teach people to use LLMs for over three years now, but it genuinely comes down to them being utterly weird and unintuitive pieces of technology that pretend to be easy to use when they aren't. The only way to get truly competent with them is to put in the time deliberately experimenting to figure out what does and doesn't work.
- alganet 1y agoWell, Claude Code is sold as an easy to use magic tool. https://www.anthropic.com/claude-code https://www.anthropic.com/claude-code > Unleash Claude’s raw power directly in your terminal. > Search million-line codebases instantly. > Turn hours-long workflows into a single command. > Your tools. Your workflow. Your codebase, evolving at thought speed. -- > Watch as Claude Code tackles an unfamiliar Next.js project, builds new functionality, creates tests, and fixes what’s broken No mention of "weird and unintuitive". It sounds like a dream. It sounds like it would accept a prompt for a niche raspberry pi skeleton and work with it. Can you really blame a beginner for buying the product and being disappointed? You should be angry at the LLM companies. While you're trying to teach people, they're working to generate tons of unsatisfied customers. And they come here, and you answer them for free? It doesn't make much sense to me.
- nexoft 1y agoI don't understand these low effort, superficial takes on complex topics from non-specialists trying to get visibility.\ This is literally 101 neural network/deep learning fact you learn when you are introduced to LLMs : it is a non-lossless compression of large dataset. [Anredj Karpathy first minutes of Intro to Large Language Models / 4Min20](https://youtu.be/zjkBMFhNj_g?t=257 https://youtu.be/zjkBMFhNj_g?t=257) Training is a compression of a huge chunk of the internet. The replies and interactions looks like a bunch of agentic models discussing with each others. Nobody is real.