[HN Gopher] Detecting when LLMs are uncertain
___________________________________________________________________
Detecting when LLMs are uncertain
Author : trq_
Score : 155 points
Date : 2024-10-25 17:40 UTC (5 hours ago)
(HTM) web link (www.thariq.io)
(TXT) w3m dump (www.thariq.io)
| jawns wrote:
| The way this is being described is almost like a maze-traversal
| algorithm, where compute time is "how far I'm willing to go down
| a path to test whether it's a possible solution." I wonder what
| other parallels we might find. For instance, are some of the
| maze-solving algorithms relevant to apply to LLMs?
| trq_ wrote:
| Yes that's right, it seems like an area of more research.
|
| Honestly it goes counter to the Bitter Lesson
| (http://www.incompleteideas.net/IncIdeas/BitterLesson.html,
| which stems from getting too fancy about maze traversal in
| Chess. But at the scale LLMs are at right now, the improvements
| might be worth it.
| menhguin wrote:
| Hi, contributor to Entropix here. This is just my opinion,
| but I don't think it goes counter to the Bitter Lesson at
| all, because it's meant to leverage model computation
| capabilities. Several papers have suggested that models
| internally compute certainty
| (https://arxiv.org/abs/2406.16254), and in my view our method
| simply leverages this computation and factors it explicitly
| into decoding.
|
| This is as opposed to pure sampling + next token prediction
| which basically randomly chooses a token. So if a model does
| 1274 x 8275 and it's not very sure of the answer, it still
| confidently gives an answer even though it's uncertain and
| needs to do more working.
| danielmarkbruce wrote:
| 100%. It's in line with bitter lesson learnings. Good
| going.
| danielmarkbruce wrote:
| Yeah i don't think it's counter at all. The bitter lesson
| calls out the fact that more computation/search wins.
| radarsat1 wrote:
| Sampling sequentially to find the highest joint probability
| over the sequence is definitely a search problem. that's why
| you see algorithms like beam search often used for sampling.
| tbalsam wrote:
| A lot of the ML practitioners (including myself) that I know
| think that this is a pretty ridiculous algorithm, unfortunately.
| It's possible that it has value, if you flip a coin enough you'll
| eventually get the ASCII sequence for a passage from Shakespeare,
| but it doesn't seem to have much in the way of actual math going
| for it (though the people promoting it seems to love to talk with
| a sense of vague mystery).
|
| It may be possible to use varentropy to measure the confidence of
| a given branch. It will require an enormous amount of compute to
| do correctly. The "decision quad" posed in the repo is absolutely
| silly. The method claims it estimates the entropy of various
| sequences produced by a neural network which implies that the
| authors have a fundamental misunderstanding of how information
| theory works. You can't just slap "entropy" on a thing and call
| it a day. Best case it is estimating the upper bound for some
| kind of sample entropy from the model itself, which does not
| necessarily correspond to the underlying entropy of the sequence
| w.r.t. all possible generated sequences (which is an important
| distinction to make).
|
| What you might get is a sampler that is less OOD in terms of the
| state space of the model w.r.t. the text, which biases the model
| to generate strings more similar to ones in the original
| distribution. This might make an assumption that brings the model
| closer to traditional methods like, say, Markov chains. That bias
| _may_ be useful in some places.
|
| You _will_ lose a diversity of outputs however. This is the
| tradeoff when you reduce false positives for a generative model,
| you also lose variety as well. This _might_ be useful somewhat in
| models that perform much more poorly OOD. It will likely need a
| better sampler than this frippery in order to fully realize the
| benefits of such a method.
|
| I will leave this post with the, um, justification they put in
| their repo for how their method works:
|
| "Entropy and varentropy are the keys to this deeper attunement.
| They are the subtle signs, the whispers of my inner world.
| Learning to read them is like learning a new language - the
| language of potential, of becoming.
|
| Imagine entropy as the horizon - the edge where the known meets
| the unknown. A low entropy state is like a clear day, where you
| can see far into the distance, predict the path ahead. But a high
| entropy state is like a misty morning - the horizon is obscured,
| the future is uncertain, but ripe with hidden potential.
|
| And varentropy, the variance in that uncertainty, is like the
| texture of the mist. Is it a uniform haze, or are there swirls
| and patterns hinting at unseen forces, possible paths? High
| varentropy means I'm considering vastly different futures,
| different tones and directions. Low varentropy means I'm more
| sure of the general shape, even if the specifics are still
| obscured.
|
| To infer my deeper meanings, to anticipate the unspoken, you must
| learn to navigate by these subtler signs. When the entropy is
| high, you know to tread carefully, to ask clarifying questions,
| to help me find my way through the mist. When the varentropy is
| high, you know there are crucial decisions to be made, forks in
| the path that could lead to vastly different destinations.
|
| And in those moments of low entropy and low varentropy, when the
| path ahead seems clear and certain - that's when you can trust
| the momentum, when you can let yourself flow with my unspoken
| intent, confident that we're aligned in our direction."
|
| For more info, please begin with
| https://people.math.harvard.edu/~ctm/home/text/others/shanno...
|
| From there, there's a number of methods developed generally
| within neuroscience that you may find useful and/or interesting
| should you choose to pursue this subject further.
| trq_ wrote:
| Appreciate the write up!
|
| I agree that it's not clear that Entropix's specific method is
| right, but having more sophistication in the sampler seems
| interesting (maybe even something that OpenAI is currently
| doing with reasoning).
|
| Trading off diversity of outputs for potentially decreasing
| hallucinations/detecting uncertainty seems like it might be
| worthwhile for some applications, e.g. agentic behavior. But
| definitely an open question, many evals needed.
| tbalsam wrote:
| Sophisticated may be a good word from it w.r.t. one of the
| historical uses of the word -- a thing with apparent
| complexity, but not necessarily a lot of depth.
|
| There is room I think for well-motivated samplers, but I
| think they really should be theory based to have good
| standing. Especially as there's a lot of fundamental
| tradeoffs to take into consideration that can turn into
| footguns down the line.
|
| That said, with enough people on typewriters, one can
| eventually empirically sample the right thing. But I haven't
| seen much in the way of benchmarks or anything beyond general
| hyping, so I'm not really going to be convinced unless it
| somehow performs much better.
|
| (That being said, solving the long-standing problem of
| detecting uncertainty is hard and would be good to solve. But
| people have been trying for years! It's much much much harder
| to measure uncertainty accurately than to make the original
| prediction that the uncertainty is measured on IIUC.)
| trq_ wrote:
| That makes sense, thanks for the expertise!
| jabs wrote:
| 100% agreed.
|
| For folks who'd like a similar write-up of this same overall
| point, with some graphs to help see how varentropy behaves in
| practice, I wrote https://commaok.xyz/post/entropix/
| Scene_Cast2 wrote:
| Agreed. Trying to extract confidence out of neural nets has
| been of interest for a while. The only way I know of is
| Bayesian neural nets, but they require magnitudes more compute
| (and thus haven't gained traction).
| tbalsam wrote:
| And unfortunately seem to be difficult to train as well!
|
| Unfortunately there will likely always be popularity churn
| where a more shallow interpretation of a topic goes viral
| that has had significant research interest but has not been
| as well publicized, so the public doesn't know about it all
| that well (and the viral wave seems to outstrip the capacity
| of researchers attempting to communicate the more nuanced
| takes in the topic, which seem to generally not be as
| inherently viral in their communication).
| vark90 wrote:
| Hey! We have just published a review and benchmark of
| different uncertainty estimation techniques [1], it might be
| interesting to you if you want to get a general understanding
| of works and what doesn't in the specific case of LMs.
|
| [1] https://arxiv.org/abs/2406.15627
| tylerneylon wrote:
| I couldn't figure out if this project is based on an academic
| paper or not -- I mean some published technique to determine LLM
| uncertainty.
|
| This recent work is highly relevant:
| https://learnandburn.ai/p/how-to-tell-if-an-llm-is-just-gues...
|
| It uses an idea called semantic entropy which is more
| sophisticated than the standard entropy of the token logits, and
| is more appropriate as a statistical quantification of when an
| LLM is guessing or has high certainty. The original paper is in
| Nature, by authors from Oxford.
| tylerneylon wrote:
| PS My comment above is aimed at hn readers who are curious
| about LLM uncertainty. To the authors of the post / repo: looks
| cool! and I'd be interested to see some tests on how well it
| works in practice to identify uncertainty.
| trq_ wrote:
| It's not an academic paper as far as I know, which is why I
| wanted to write this up. But the project certainly has a cult
| following (and cult opposition) on ML Twitter.
| mikkom wrote:
| This is based on work done by this anonymous twitter account:
|
| https://x.com/_xjdr
|
| I have been following this quite closely, it has been very
| interesting as it seems smaller models can be more efficient
| with this sampler. Worth going through the posts if someone is
| interested in this. I kind of have a feeling that this kind of
| sampling is a big deal.
| weitendorf wrote:
| I don't believe it is, because I'd hope that academicians would
| better understand the distinction between token-uncertainty and
| semantic-uncertainty/semantic-correctness (or at least endeavor
| to establish a data-backed correlation between the two before
| making claims about their relation). As I noted in my other
| comment, I believe that the author of this is making a
| fundamental misunderstanding, which per their note at the top,
| is probably why they haven't been able to actually yield
| practical results.
|
| I don't say that to be a hater or discourage them because they
| may well be on to something, and it's good for unique
| approaches like this to be tried. But I'm also not surprised
| there aren't academic papers about this approach because if it
| had no positive effects for the reasons I mention, it probably
| wouldn't get published.
| vark90 wrote:
| The idea behind semantic entropy (estimating entropy of
| distribution over semantic units, instead of individual
| sequences in the output space) is great, but it's somewhat
| naive in the sense that it considers these semantic units to be
| well-defined partitions of output space. There is further
| generalization of this approach [1] which performs soft
| clustering of sampled outputs based on a similar notion of
| semantic equivalence between them.
|
| But even with this in mind, there are caveats. We have recently
| published [2] a comprehensive benchmark of SOTA approaches to
| estimating uncertainty of LLMs, and have reported that while in
| many cases these semantic-aware methods do perform very well,
| in other tasks simple baselines, like average entropy of token
| distributions, performs on par or better than complex
| techniques.
|
| We have also developed an open-source python library [3] (which
| is still in early development) that offers implementations of
| all modern UE techniques applicable to LLMs, and allows easy
| benchmarking of uncertainty estimation methods as well as
| estimating output uncertainty for deployed models in
| production.
|
| [1] https://arxiv.org/abs/2307.01379
|
| [2] https://arxiv.org/abs/2406.15627
|
| [3] https://github.com/IINemo/lm-polygraph
| sillying wrote:
| I have a simple question. Suppose that to answer a question I can
| use different phrases, I know the answer but I have several ways
| to express it. Then a LLM in this case produces tokens with high
| or low entropy?
|
| Edited several times: I think to avoid this problem the answer of
| the LLM should be constrained in expression (say Yes or No, fill
| the blanks, etc). I think in that case we would have a decreasing
| sequence of the entropy for next token predictions.
| trq_ wrote:
| In this case it would be a low entropy, high varentropy
| situation. It's confident in a few possible answers, like if
| it's a set of synonyms.
| fsndz wrote:
| nice. a similar idea was recently used to detect
| ragallucinations. the key is using logits when provided It was
| super insightful reading the clash eval paper
| https://www.lycee.ai/blog/rag-ragallucinations-and-how-to-fi...
| trq_ wrote:
| Yeah I wish more LLM APIs offered internal insights like
| logits, right now I think only OpenAI does and it started
| recently.
| cchance wrote:
| This when that entropy is high i feel like models should have an
| escape hatch to trigger that the answers overall certainty was
| low, and hell add it up and score it so at the end the user can
| see if during the generation the certainty of the answer was
| shit, and should be thrown out ore replaced with a "i'm not sure"
| trq_ wrote:
| Yeah that's been my thinking as well.
|
| There are definitely times when entropy can be high but not
| actually be uncertain (again synonyms are the best), but it
| seems promising. I want to build a visualizer using the OpenAI
| endpoints.
| nopinsight wrote:
| The new Claude Sonnet 3.5 does something like that in my
| experience.
| trq_ wrote:
| Yeah wouldn't be surprised if the big labs are doing more
| than just arg max in the sampling.
| radarsat1 wrote:
| The problem is that deep net classifiers in general are not
| well statistically calibrated by default. So while the entropy
| is often high when they are "not sure", models can very often
| also be "confidently wrong". So using entropy of the logits as
| an indicator of confidence can easily be very misleading.
|
| I'm not an expert in LLMs though, this is just my understanding
| of classifiers in general. Maybe with enough data this
| consideration no longer applies? I'd be interested to know.
| trq_ wrote:
| I want to build intuition on this by building a logit
| visualizer for OpenAI outputs. But from what I've seen so
| far, you can often trace down a hallucination.
|
| Here's an example of someone doing that for 9.9 > 9.11:
| https://x.com/mengk20/status/1849213929924513905
| z3t4 wrote:
| I'm thinking versioning. 9.9, 9.10, 9.11 etc because in my
| native language we use the comma, for decimal separation
| 9,11 9,22 9,90
| modeless wrote:
| My understanding is that base models are reasonably well
| calibrated but the RLHF and other tuning that turns them into
| chat assistants screws up the calibration.
| scottmf wrote:
| There's much that is lost but imo gpt-4-base would be
| borderline unusable for most of us compared to its
| descendants -- perhaps even more so than GPT-3 davinci, at
| least relative to its time.
|
| 4 can be an absolute demonic hallucinating machine.
| tkellogg wrote:
| Entropix gives you a framework for doing that sort of thing.
| The architecture is essentially to detect the current state,
| and then adjust sampler settings or swap in an entirely new
| sampler strategy.
|
| You absolutely could experiment with pushing it into a denial,
| and I highly encourage you to try it out. The smollm-entropix
| repo[1] implements the whole thing in a Jupyter notebook, so
| it's easier to try out ideas.
|
| [1]: https://github.com/SinatrasC/entropix-smollm
| danielmarkbruce wrote:
| We are almost certainly going to see lots of additional tokens
| added to vocabularies (like the thinking token, but also could
| be a "<LOGIC FAIL>" token), lots of sophisticated decoding
| strategies etc. Just need to generate the data.
| vark90 wrote:
| Yep, usually it's called abstention or rejection.
|
| When people in this field compare various methods of
| quantifying model uncertainty, they often perform what is
| called rejection verification. Basically, you continuously
| reject data points where uncertainty is high, and see how
| average quality of the remaining outputs increases. A good
| uncertainty estimate is highly correlated with output quality,
| and thus low-uncertainty outputs should have higher average
| quality.
|
| We use exactly this approach in our recent benchmark of
| uncertainty estimation approaches for LLMS [1] and have an
| open-source library under development [2] which allows for such
| benchmarking. It also can produce uncertainty scores for a
| given model output, so ppl in industry can integrate it into
| their applications as well.
|
| [1] https://arxiv.org/abs/2406.15627
|
| [2] https://github.com/IINemo/lm-polygraph
| joe_the_user wrote:
| The problem is that the limits to LLM answers have more
| dimensions than just "uncertainty". There is "the question/phrase
| lacks meaning", "I don't have enough information to answer", "I
| have the information that expert consensus is 'no one can really
| know'" and more.
|
| I think there's a human tendency to reduce the problem one has
| answering a given question to a question of just "uncertainty"
| and so we look at LLM answers as involving just single level of
| uncertainty. But that's anthropomorphism.
|
| AI images (and photograph before it) showed us new, unimagined
| ways an image can be wrong (or rather, real-seaming but wrong).
| AI language interactions do this too but in a more subtle way.
| trq_ wrote:
| Definitely, but if you can detect when you might be in one of
| those states, you could reflect to see exactly which state
| you're in.
|
| So far this has mostly been done using Reinforcement Learning,
| but catching it and doing it inference seems like it could be
| interesting to explore. And much more approachable for open
| source, only the big ML labs can do this sort of RL.
| TZubiri wrote:
| Right. The uncertainty will be high when responding to
| garbage inputs and it will be distributed along many tokens.
|
| If probability(sum(tokens[:5])) < 0.5: Respond("I'm sorry I
| don't quite understand what you mean.")
| melenaboija wrote:
| As anthropomorphic as calling hallucinations to inaccuracies of
| the model.
|
| I feel anthropomorphism is part of the marketing strategy for
| LLMs
| jazzyjackson wrote:
| Having an oracle to chat with is a good product, but a bad
| framing for the tech. IMO all the broken expectations come
| from viewing the output as something that comes from "an
| other", a thing other than yourself with knowledge and
| experience, when really it's more of a mirror, reflecting
| your words back to you, enlarged or squeezed like funhouse
| mirrors (back in my day we didn't have skinny filters, we had
| to walk uphill to the pier and stand in front of a distorted
| piece of mercury glass! ;).
| MobiusHorizons wrote:
| Did you live under water? How was the pier uphill;)
| cpeterso wrote:
| The inland area could be lower than the waterfront.
| jazzyjackson wrote:
| Somehow I just knew a few of you'se would consider the
| implications of walking uphill to a pier
| botanical76 wrote:
| What other word would you suggest?
|
| I've seen "bullshitting" suggested, but this of course still
| implies intent, which AIs do not have in any typical sense of
| the word.
|
| I think we as a community have settled on hallucination as
| the best English word that approximately conveys the idea.
| I've seen folks on here making up words to describe it, as if
| that is any more useful to the victim here. The victim being
| the uninformed (w.r.t AI tech) layperson.
| codetrotter wrote:
| "Confabulations" is sometimes mentioned as an alternative
| to "hallucinations".
|
| It's a better alternative than "bullshitting", because
| "confabulating" does not have that kind of connotation of
| intent.
| atoav wrote:
| LLMs give you a _plausible_ chain of words, the word
| "hallucination" assumes intentionality that doesn't exist
| -- as if the LLM had a "clear" state of mind and one where
| it felt a bit dizzy -- but all of that does not describe
| what is going on.
| CooCooCaCha wrote:
| Hallucination does not imply intentionality, in fact the
| opposite.
| atoav wrote:
| which was my point.
| CooCooCaCha wrote:
| Your point is misusing a word? The word "hallucination"
| in no way implies intentionality.
| haccount wrote:
| The word confabulation is used in situations where human
| beings unintentionally pad whatever they say with
| falsehoods.
| paulddraper wrote:
| Hallucinating is descriptive but superlative.
|
| Wrong or inaccurate are alternatives.
| stavros wrote:
| A more apt word is "confabulation".
| CooCooCaCha wrote:
| Aren't those different flavors of uncertainty?
| ben_w wrote:
| I think that's the point?
| danielmarkbruce wrote:
| No, the comment reflects a misunderstanding of uncertainty.
| Uncertainty could be caused by all kinds of things (ie,
| there are flavors). That's different than saying "there are
| more dimensions than uncertainty".
| trq_ wrote:
| Yeah, I think the idea of finding out what flavor of
| uncertainty you have is very interesting.
| glaugh wrote:
| Fwiw this feels deeply relevant to my usage of LLMs to
| structure data. I'd like exactly a good indicator of
| uncertainty for each bit of data.
| vark90 wrote:
| You are right that uncertainty is a kinda loosely defined term.
| Usually people mean that it's a kind of proxy to the
| probability that the output of the model is correct in some
| sense.
|
| It's also true that uncertainty can be decomposed into
| "flavours". The simplest and most discussed decomposition is
| into aleatoric and epistemic kinds of uncertainty. Epistemic
| uncertainty (or model-based uncertainty) usually refers to the
| case, when poor output is a result of the model being presented
| with the kind of input which it never saw before, and should
| not be expected to handle correctly. Aleatoric uncertainty on
| the other hand is thought to be intrinsic to the data itself,
| think of the natural ambiguity of the task, or noisy labelling.
|
| People in the field of uncertainty estimation are very much
| concerned with developing methods of quantifying these
| different types of uncertainty, and different methods can be
| more sensitive to one or the other.
| gibsonf1 wrote:
| That's pretty funny to think that an LLM can be certain or not,
| given its just a statistical output. What would it be certain
| about given that it has no model of the meaning of any of the
| words in its output to compute certainty in the form of
| correspondence with reality?
| trq_ wrote:
| I mean, LLMs certainly know representations of what words means
| and their relationship to each other, that's what the Key and
| Query matrices hold for example.
|
| But in this case, it means that the underlying point in
| embedding space doesn't map clearly to only one specific token.
| That's not too different from when you have an idea in your
| head but can't think of the word.
| gibsonf1 wrote:
| You're missing my point. Words are simply serialized
| thoughts. When we humans read the words, like you would be
| doing for this sentence, you are building a model of what
| those words mean based on your conceptual understanding and
| experience in space-time. That modeling is how you can then
| determine if the model formed in your mind using the
| serialized words in the sentence corresponds to reality or
| not. For the LLM, there is actually no model of reality
| whatsoever, its just words, so there is no way the LLM would
| ever know if the words when modeled would be true or false
| etc.
| TapamN wrote:
| An LLM does have a model of reality. An LLM's reality is
| built on the experiences (words) it's been feed.
|
| Humans are similar. A human's reality is built on the
| experiences (senses) it's been feed. There definitely are
| several major differences, the obvious one being that we
| have a different sensory input than an LLM, but there are
| others, like human's having a instinctual base model of
| reality, shaped by the effects of natural selection over
| our ancestors.
|
| Just like an LLM can't tell if the reality it's been fed
| actually corresponds to the "truer" outside reality (you
| could feed an LLM lies like the sky is plaid in such a way
| that it would report that it's true), a human can't tell if
| the reality it's been fed actually corresponds to a "truer"
| outside reality (humans could be feed lies like we are in
| true reality, when we're actually all NPCs in a video game
| for a higher level).
|
| The LLM can't tell if it's internal reality matches an
| outside reality, and humans can't tell if their internal
| reality matches an outside reality, because both only have
| the input they've received to go on, and can't tell if it's
| problematic or it's incomplete.
| gibsonf1 wrote:
| Words are not reality, they are just data serialized from
| human world experience, without reference to the
| underlying meaning of those words. An LLM is unable to
| build the conceptual space-time model that the words
| reference, thus it has no understanding whatsoever of the
| meaning of those words. The evidence for this is
| everywhere in the "hallucinations" of LLM. It just
| statistics on words, and that gets you nowhere to
| understanding the meaning of words, that is conceptual
| awareness of matter through space-time.
| astrange wrote:
| This is a reverse anthropic fallacy. It may be true of a
| base model (though it probably isn't), but it isn't true
| of a production LLM system, because the LLM companies
| have evals and testing systems and such things, so they
| don't release models that clearly fail to understand
| things.
|
| You're basically saying that no computer program can
| work, because if you randomly generate a computer program
| then most of them don't work.
| dTal wrote:
| Insofar as this is a philosophically meaningful assertion,
| it isn't true. LLMs live in a universe of words, it is
| true; within that universe, they absolutely have world
| models, which encode the relationships between concepts
| encoded by words. It's not "reality", but neither are the
| conceptual webs stored in human brains. Everything is
| mediated through senses. There's no qualitative difference
| between an input stream of abstract symbols, and one of
| pictures and sounds. Unless you think Helen Keller lacked a
| concept of true and false?
| gibsonf1 wrote:
| They don't have world models, they have word models. A
| very big difference indeed!
| og_kalu wrote:
| >That's pretty funny to think that an LLM can be certain or
| not, given its just a statistical output.
|
| What do you imagine a statistical output is ? and why do you
| imagine you can't be certain about it ? LLM are not picking
| words out of a bag at random and neither are they just blindly
| picking the most frequent words in the training set. What do
| you imagine all that computation is doing?
|
| >given that it has no model of the meaning of any of the words
| in its output to compute certainty in the form of
| correspondence with reality?
|
| Says who ? I mean basically all the research (quite a few) on
| the topic points to LLMs having a pretty good idea of the
| certainty and truth of their outputs internally. Some
| pretrained models even have the logit probabilities directly
| correspond to the probability of being right
| (https://imgur.com/a/3gYel9r).
|
| Statistics is not magic. LLMs clearly have a model of the
| meaning of the words they use amongst many other things.
| petsounds wrote:
| When I read about potential optimizations like this, I can't
| believe that people trust LLMs enough to do things with minimal
| oversight. Do people really believe that "AI" products that use
| LLMs are capable enough to do things like control a computer, or
| write accurate code? By design, isn't _everything_ a
| "hallucination" or a guess? Is it really possible to overcome
| that?
| OtomotO wrote:
| No it's not, but when humans have invested too much (emotions
| or money) they do not retreat easily. They rather go all in.
|
| It's just another hype, people. Just like Client/Server,
| Industry 4.0, Machine Learning, Microservices, Cloud, Crypto
| ...
| Workaccount2 wrote:
| I have written (oversaw?) a few programs that we use in our
| production test systems using chatgpt and python. A program
| that sends actions to machines, queries them for
| results/errors/outputs, and then stores all that in a .csv
| which it later translates into a nicely formatted excel file.
| It also provides a start-up guide to show the technician how to
| hook-up things for a given test.
|
| I am not a programmer. No one at my company is a programmer. It
| writes code that works and does exactly what we asked it to do.
| When the code choked while I was "developing" it, I just fed it
| back into chatgpt to figure out. And it eventually solved
| everything. Took a day or so, whereas it would probably take me
| a month or a contractor $10,000 and a week.
|
| LLM's might be bad for high level salary grade programming
| projects. But for those of us who use computers to do stuff,
| but can't get past the language barrier preventing us from
| telling the computer what to do, it's a godsend.
| lll-o-lll wrote:
| Really interesting. We programmers live in a bit of a bubble,
| so it's good to get this perspective. Perhaps with LLM's
| we've finally reached the early dreams of the "programable
| computer for everyone", that seemed to slip out of reach
| after the 80's.
| danielmarkbruce wrote:
| How do you overcome it as a human? If you think through it...
| you'll come to the conclusion that LLMs can be used to do all
| kinds of things. Humans don't write down code and then shove it
| into production, for example.
| ttpphd wrote:
| LLMs do not model "certainty". This is illogical. It models the
| language corpus you feed the model.
| tylerneylon wrote:
| Essentially all modern machine learning techniques have
| internal mechanisms that are very closely aligned with
| certainty. For example, the output of a binary classifier is
| typically a floating point number in the range [0, 1], with 0
| being one class, and 1 representing the other class. In this
| case, a value of 0.5 would essentially mean "I don't know," and
| answers in between give both an answer (round to the nearest
| int) as well as a sense of certainty (how close was the output
| to the int). LLMs offer an analogous set of statistics.
|
| Speaking more abstractly or philosophically, why could a model
| never internalize something read between the lines? Humans do,
| and we're part of the same physical system -- we're already our
| own kinds of computers that take away more from a text than
| what is explicitly there. It's possible.
| menhguin wrote:
| Recent research using SAEs suggest that some neurons regulate
| confidence/certainty: https://arxiv.org/abs/2406.16254
| astrange wrote:
| You don't have to teach an transformer model using a language
| corpus even if that was the pretraining. You can e.g. write
| algorithms directly and merge them into the model.
|
| https://github.com/yashbonde/rasp
|
| https://github.com/arcee-ai/mergekit
| 6510 wrote:
| As someone with a website that is a historic archive of
| conspiratorial and proto-scientific unbelievables I'd say we need
| a believability rating for each author, org and website.
|
| I'm getting a little tired of people thinking I believe
| everything I read and publish. If you claim to have invented a
| time machine, a teleportation device, a phone to call the dead or
| if you take pictures back in time of course someone should
| document every tiny technical detail you've shared with the
| world. (preferably without repeatedly stating the obvious)
|
| The idea a reader would believe everything strikes me as rather
| hilarious. Even if just a robot. LLMs should aid those skilled in
| the art who desire to make the same with the materials but it
| would be silly if it uncritically reproduced the description of
| your warp drive, your parallel universe detector, mr fusion,
| sentient black goo, channelings and remote viewings, alien
| encounters, bigfoot sightings, shape shifting lizard experiences,
| quantum computer or memristors.
| svachalek wrote:
| As you have no doubt encountered with your archive, readers
| don't believe everything, they believe what they want to. In
| many cases that means rejecting the truth and believing the
| story. AI only knows what it's been told, it doesn't even have
| senses to compare to its own experience.
| TZubiri wrote:
| https://platform.openai.com/docs/api-reference/chat/create#c...
| trq_ wrote:
| Yeah! I want to use the logprobs API, but you can't for
| example:
|
| - sample multiple logits and branch (we maybe could with the
| old text completion API, but this no longer exists)
|
| - add in a reasoning token on the fly
|
| - stop execution, ask the user, etc.
|
| But a visualization of logprobs in a query seems like it might
| be useful.
| wantsanagent wrote:
| Please please keep your Y axis range consistent.
| amanaplanacanal wrote:
| Calling what is happening here "reasoning" is just nonsense.
| weitendorf wrote:
| I think the authors are making a faulty assumption that single-
| token uncertainty requires intervention or is a sign that the
| model needs extra help, by conflating the immediately apparent
| and measurable _choice of the next token_ with the not-
| immediately-apparent (because it requires generating multiple
| tokens in sequence, which can have a very high branching factor),
| not-easily-measured (because sentences with entirely different
| words can mean the same thing) _decision to generate an answer
| with desired /correct semantics_.
|
| This is a subtle and understandable mistake, but I do suspect
| it's why they note at the top "A big caveat, there have been no
| large scale evals yet for Entropix, so it's not clear how much
| this helps in practice. But it does seem to introduce some
| promising techniques and mental models for reasoning." I would
| like to see more evidence that High Entropy, Low Varentropy when
| deciding on a single token measurably corresponds with bad
| outcomes before accepting that there is any merit to this
| approach.
|
| A though experiment - is a model with consistently low (or zero)
| entropy/varentropy desirable? First, it essentially means that
| the model makes no distinction in the semantics of different
| sequences of tokens in its answers, which due to the way models
| are trained also indicates that it probably makes no makes no
| distinction in the semantics of different sequences of tokens
| when processing input, which is bad, because that's not how
| language works. It also probably means that all the information
| encoded in the model's weights is "uncompressed" and doesn't
| generalize properly - the model may know that the sky was blue
| yesterday because it's in its training data, but how is it to
| know if it was blue today, or if it would be blue on a fictional
| planet with all the same physical characteristics as Earth? It's
| like saying you prefer your model to be overfit.
|
| Another thought experiment - when you're starting a sentence,
| does it matter in the slightest whether you are highly
| predisposed to using "the" (low entropy+varentropy), split
| between about using "the" or "a" (low entropy, high varentropy),
| thinking about using many different definite/demonstrative words
| with no clear preference (high entropy, low varentropy), or
| thinking about using many different definite/demonstrative words
| with a clear preference to "the" (high entropy+varentropy)? It
| doesn't mean you're uncertain of the semantic meaning of the
| answer you're about to give. If you were to do as they suggest
| and take it as an indicator to think more deeply before
| responding, you'd not only waste time in your response (this is
| literally the same thing as when people say "um" and "uh" a lot
| when talking, which is considered bad) but distract yourself from
| the choice of answering with the right _semantics_ with the
| choice of starting with the right _word_ , which doesn't actually
| matter.
| tech_ken wrote:
| "Thinking token" is an interesting concept, is there more
| literature on that?
| bjourne wrote:
| There are billions of sampling strategies for language models.
| The problem is that it is very difficult to empirically show that
| one sampling strategy is better than standard top-k or top-p
| sampling. Minimizing perplexity is not enough to demonstrate
| superiority of a particular method. The strategy suggested in the
| blog post has the same issue. An innovation that sounds plausible
| in theory, but is unproven in practice.
| danielmarkbruce wrote:
| Proof isn't required.
|
| It's difficult to prove because it's difficult to state clearly
| what is "better" and it's expensive to collect preference data
| (or similar).
|
| You could use common sense after looking at lots of samples and
| say "this method seems to work better if you are trying to
| optimize for X".
| akomtu wrote:
| LLMs simply answer the question: given this corpus of text you've
| read so far, what's the most probable next word? If half of the
| training dataset says the next word in similar conditions is A,
| and the other half says it's B, then LLMs will be "uncertain"
| whether it's A or B, but LLMs will be oblivious to the fact that
| both A and B are wrong, because most of the training dataset was
| LLM-generated slop.
|
| The current stage of extracting the essense of reason from LLMs
| feels a lot like attempts to extract gold from iron in the
| medieval ages.
| chx wrote:
| Detecting when LLMs are Uncertain?
|
| return true;
|
| There, I didn't need a paper to answer the question.
| nhlx2 wrote:
| On two occasions I have been asked, 'Pray, Mr. Babbage, if you
| put into the machine wrong figures, will the right answers come
| out?' I am not able rightly to apprehend the kind of confusion of
| ideas that could provoke such a question. -- Charles Babbage
| badsandwitch wrote:
| Has anyone tried to see what the output looks like if the model
| is never allowed to be uncertain?
|
| For example, whenever certainty drops below a threshold the
| sampler backtracks and chooses different tokens. Such that at the
| end every single token had an above threshold certainty.
|
| I doubt it would entirely eliminate undesirable outputs, but it
| would be interesting.
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