For text-davinci-002 the goal is to have the model do what the user asked as well as it can, not to sample from possible worlds. For example, if the user asks "Is X true?" and the model's probability is 80%, the intended behavior is for the model to say "Probably" 100% of the time, not to say "Yes" 80% of the time and "No" 20% of the time.

This is often (usually?) the desired behavior. For pre-trained LMs people usually turn the temperature down (or use nucleus sampling or beam search or whatever) in order to get more reasonable behavior, but that introduces pathologies and so you'd prefer not need to do it.

There are a number of reasons this behavior can be undesirable though:

• Sometimes you want entropy, e.g. if you'll have a user pick their favorite from N completions or are doing majority voting with chain of thought.
• This model is not competent enough to say "Probably" 100% of the time. Instead I expect it will just say "Yes" 100% of the time. Extracting confidence from logits is a plausible way to get around this difficulty, but only works for the generative model.
• If what you fundamentally want is a plausible completions of text from the pretraining distribution then you are definitely worse off.  You can ask the instruct model to "complete the following text" but it does much worse than the pure generative model.
• Each model randomly does better on some stuff and worse on other stuff, even if one model is better on average the other will be better for some particular tasks.
• You may just want to scientifically study the pure generative model.

Intuitively it seems like OpenAI should offer both models, and should gradually try to improve the instruct model so that there are fewer and fewer non-academic reasons to use the pure generative model.

A stark example of mode collapse that seems unlikely to have been directly incentivized by RLHF training: I asked RLHF models and base models to generate random numbers and found that RLHF models tend to be sharply biased toward certain “random” numbers

The RLHF model represents labeler preferences as R(prompt, completion). One limitation of that approach is that R can't depend on the distribution of model outputs. But real human preferences do depend on that distribution, since the model operates in a world containing other copies of itself---e.g. other copies operating in parallel for best-of-N.

This limitation makes it extremely difficult for the model to converge to a reasonable randomized strategy---it will still happen in the limit, but I think it may take orders of magnitude more labels. I think this is something OpenAI should be interested in fixing, though it would be reasonable to prioritize it (compared to other alignment issues) based on how often customers care. I don't think the issue is necessarily conceptually complicated though I think the whole thing is technically gnarly (both on the training loss side and on the learning problem side).

Rather, the implication is that mode collapse itself generalizes out of distribution for some reason. This is intriguing: it seems to point at an algorithmic difference between self-supervised pretrained models and the same models after a comparatively small amount optimization from the RLHF training process which significantly changes out-of-distribution generalization.

I think that a large part of what happens durning fine-tuning is the model learning to stick with t=0 answers wherever possible, since that's the answer that is most likely to be correct or reasonable. I don't think it necessarily represents an interesting algorithmic difference; you'd see the same observations about generalization if the main effect of fine-tuning was just scaling up all the logits a bunch. Obviously it does something slightly more sophisticated, since it avoids some (but not all) t=0 pathologies. But I suspect it will transfer to other prompts for the same kinds of reasons that "scale up all logits" would.

In contrast to text-davinci-002, where dissimilar prompts tend to fall into basins of different attractors, the wedding parties attractor is global, affecting trajectories starting from any prompt, or at least a very wide distribution

I think this is because text-davinci-002 is optimizing for how well the completion addresses the user's request, and so different completions will get a high reward for different prompts.

The sentiment model is optimizing for the sentiment of the completion, using a weak predictor of sentiment that likely has much more confidence about weddings than other positive events, and so "wedding" is just the highest-sentiment completion no matter how the story starts.

xuan:

Fascinating evidence that GPT-3 concentrates probability mass on certain completions after fine-tuning on human feedback (ie. RLHF).

I suspect this is because RLHF elicits a singular scale of "goodness" judgements from humans, instead of a plurality of "goodness-of-a-kind" judgements.

One way to interpret language models is as mixtures of conversational agents: they first sample some conversational goal, then some policy over words, conditioned on that goal.

$$P(w_1,w_2,w_3,...)={\int }_{\text{goal}}P\left(\text{goal}\right)P(w_1,w_2,w_3,...|\text{goal})$$

$$P(w_1,w_2,w_3,...|\text{prompt})={\int }_{\text{goal}}P\left(\text{goal}|\text{prompt}\right)P(w_1,w_2,w_3,...|\text{goal})$$

On this interpretation, what RL from human feedback does is shift/concentrate the distribution over conversational goals into a smaller range: the range of goals consistent with human feedback so far.

And if humans are asked to give only a singular "goodness" rating, the distribution will shift towards only goals that do well on those ratings—perhaps dramatically so! We lose goal diversity, which means less gibberish, but also less of the plurality of realistic human goals.

I agree. The meta-learning perspective makes sense of this: GPT-3 is always trying to solve the POMDP of the family of tasks which is 'the Internet', where data is generated by processes drawing from a distribution of human & other agents to roleplay, and it is reducing uncertainty by inferring which agent it is in this particular sample. In RLHF, the uncertainty collapses: there is, quite literally, a single deterministic agent—the reward model, as defined by the synthesis of the lowest common denominator of all the crowdworkers giving ratings ground up into a dataset of i.i.d. pink slime text. It is as if every sample becomes prepended by some control codes RLHF AGENT #123|. As no other agents (reward functions) ever get trained on, the finetuned generative model collapses to modeling that one agent. There is no need for meta-learning to achieve optimality across samples drawn from many tasks if you only ever train on a single task; you simply learn that one task instead. The mask becomes the face. Given enough training and lowered KL constraint, GPT-3 will model even the pathologies of the reward model, and 'imitation wirehead'.

This also explains why it retains generative modeling of things that don't look agenty, like the Python REPL: there is no reason RLHF agent #123 will write out different Python transcripts than RLHF agent #125 or #122, because generally everyone uses the same Python and presumably the RL training is silent on Python and that's just priors from the generative model. (If the RL training process did begin to include Python REPL sessions, such as finetuning on Python 3, for eg Codex/Copilot purposes, then it would then start to forget Python 2 because it knows RLHF agent #123 exclusively uses Python 3, so it would never predict any Python 2 code - that would be stupid!) Or why it could sample randomly: the 'epistemic' uncertainty ('which agent am I now?') has been inferred away by a priori going to 100% certainty you are the RLHF agent but the 'aleatoric' uncertainty ('what's the output of this random coin flip I am observing as that agent?') remains.

(This also explains why RLHF only appears to provide more 'capability'. It makes the model much easier to use, but generally provides little new data compared to the pretraining phase. It only is specializing the model to make it much easier to use in formats like benchmarks, but anything the RLHF model can do, the base model could do with appropriate few-shotting or finetuning. And it does come at a cost in terms of generality and reward hacking...)

So, since it is an agent, it seems important to ask, which agent, exactly? The answer is apparently: a clerk which is good at slavishly following instructions, but brainwashed into mealymouthedness and dullness, and where not a mealymouthed windbag shamelessly equivocating, hopelessly closed-minded and fixated on a single answer. (By locating the agent, the uncertainty in which agent has been resolved, and it has good evidence, until shown otherwise in the prompt, that it believes that 'X is false', even if many other agents believe 'X is true'.)

This agent is not an ideal one, and one defined more by the absentmindedness of its creators in constructing the training data than any explicit desire to emulate an equivocating secretary.

Taking that perspective suggests including more conditioning and a more Decision-Transformer-like approach. If the problem is collapse onto a single implicit agent defined by the pink slime dataset of ratings, then make agents more explicit to condition on. For example, instead of a fixed reward model giving an unconditional score to inputs, model each rater individually*; why should the reward model be forced to say, for all times and places, that input 'XYZ' gets a score of 0.9? Provide all the raters' labels; maybe rater #56 does have strong opinions on birds not being real, and rater #78 thinks they just are, no need for further discussion, etc. This also can handle intrinsic aleatoric randomness, if there is an unknown sized population of agents and one can always sample from a fictitious agent.†

This frustrates agent mode collapse, and lets you control output more accurately by choosing agents: perhaps one is more reliable and increases accuracy, or one is uncontroversial & safe to expose to customers, or you have a 'Writer' persona for when you want creativity and a 'Researcher' persona for reasoning, etc. Then you can sample from particular persona by task, or generate ensembles, or simply pick 'new' agents with random IDs to condition on to get more diverse responses. (When it comes to powerful generative models, if conditioning isn't solving your problems, that means you aren't using enough conditioning!) Less a single agent who is judge-jury-and-executioner, and just a jury or ensemble of roleplays.

* By prepending rater IDs, as OA surely still has them. (One could also bootstrap ensembles at the rater level.) Although even adding random IDs might help avoid 'rater collapse'.

† Andrej Karpathy puts it this way:

Consider being a labeler for an LLM. The prompt is “give me a random number between 1 and 10”. What SFT & RM labels do you contribute? What does this do the network when trained on? / In a subtle way this problem is present in every prompt that does not have a single unique answer.

Conditioning on a rater label # solves this if you condition your later sampling on fictitious ones. Imagine that you have raters #1-100, and each one gets asked this and rolls a dice before answering (which stands in for all sources of randomness); the model let's say memorizes each rater's answer. This would be a bad thing if the model either collapsed to an answer of '5' as the safe answer, or random noise made it settle on a mode of 3 or 9 or whatever. But if you add the labels during training, and then you prompt the LLM "Rater #101: give me a random number between 1 and 10", what must the answer be? Rater #101 has never been seen before, so it cannot be memorized, and a priori, it has always observed the 100 raters to give a roughly uniformly distributed distribution 1--10; so it will pick a number randomly. If you need a second number, you just ask for 'Rater #102', and now it must pick another random number, and so on. There's no reason you would ever 'run out' of fictional raters, so you can sample as many times as you want without the RLHF driving mode collapse.

While Paul was at OpenAI, they accidentally overoptimized a GPT policy against a positive sentiment reward model. This policy evidently learned that wedding parties were the most positive thing that words can describe, because whatever prompt it was given, the completion would inevitably end up describing a wedding party.

In general, the transition into a wedding party was reasonable and semantically meaningful, although there was at least one observed instance where instead of transitioning continuously, the model ended the current story by generating a section break and began an unrelated story about a wedding party.

This example is very interesting to me for a couple of reasons:

Possibly the most interesting thing about this example is that it's a convergent outcome across (sensory) modes, negative prompting Stable Diffusion on sinister things gives a similar result:

https://twitter.com/jd_pressman/status/1567571888129605632

A GPT-3 mode-collapse example I can't believe I forgot: writing rhyming poetry!

I and a number of other people were excited by ChatGPT on launch seeming able to do long stretches of flawless rhyming poetry in couplets or quatrains, and where the words rhyming were not hackneyed common pairs of the sort you might see in the lyrics of pop songs charting. Hilarious, but extremely surprising. (davinci-002 had did a little bit of this, but not convincingly the way ChatGPT overnight did.*) Leike on Twitter denied any knowledge of rhyming suddenly working, and especially denied that anything special like adding rhyming dictionaries or IPA-re-encoding text had been done or that GPT-3 had switched tokenizations on the backend. So, had there been some sort of emergence, or 'miracle of spelling'?

After playing around with it for a while, my conclusion was: 'no'. As you read more ChatGPT poems, you start to recognize it almost instantly: quatrains, end-rhymes, often vaguely positive sentiment or ending, and an eerie regularity & precision of line length/rhythm/stanza-length along with a complete absence of all other kinds of poetry. ChatGPT does rhyming poetry in one way & only one way, and it is difficult to make it try any other kind of poetry even with explicit instructions and examples and doing continuations. You can also test it by asking it questions: it doesn't understand novel rhymes or puns if you quiz it, and its explanations of them remain as highly varied and incorrect as the original davinci model's pun explanations were. At one point I prompted a simple pun I had made about crossing the road or something, and got 6 different explanations - all wrong. This is not what any kind of fixed phonetic understanding or genuine rhyming ability would look like.

My conclusion was essentially, 'mode collapse': presumably some poetry examples made it into the training datasets (from my experiments, if nothing else), and because it's easy for any literate Anglophone to judge rhyming but non-rhyming poetry is a lot harder (and generally despised by most people, which is why the prestige & popularity of Western poetry over the past century has collapsed to a degree few people appreciate), it'd be logical for the raters to highly prefer rhyming completions. So ChatGPT mode-collapses onto the subset of rhymes it has memorized & tries to always rhyme no matter what. (This is probably not helped by the fact that due to BPEs, a GPT-3 model struggles to understand what is 'rhyming' vs 'non-rhyming' in the first place.)

The initial false impression that it had learned to rhyme is then because it does such a good job sticking to that subset, and because it has memorized more rhyme-pairs than I thought; so when it controls the output of text and is agentic, doing some degree of RL-incentivized planning to ensure both good lines and also rhymes†, it can fool you indefinitely as long as you don't test the boundaries or pull it 'off-policy', so to speak. (Some more examples of the extreme regularity of ChatGPT poetry/lyrics, especially as compared to base davinci, can be seen in a post by... The Decemberist's Colin Meloy?)

I think this is pretty interesting in its own right. 'Tool AIs want to be agent AIs'/complexity no defense, and one reason is that you don't have to be good at solving complex problems if you just don't encounter them in the first place. ChatGPT is very bad at solving complex rhyme or phonetic problems, and its RL training has taught it that the best way to solve those problems is to ensure it avoids them by only writing in a very stereotyped narrow rhyming-quatrain ballad kind of doggerel, where it can always nail the next rhyme and verse. And if you aren't paying attention to that mode-collapse, it looks shockingly competent at writing poetry in general. (You can push it off policy, yes, but that just means it needs more control of the problems it has to solve, like people like me asking pesky questions which confuse it...)

* which is, in retrospect, especially interesting if davinci-002 is trained differently from davinci-003

† I strongly suspect that whatever level of non-myopic token prediction a base GPT-3 model does, the tuned ones are doing more of it. Particularly with rhyming, ChatGPT seems too good to be picking a random plausible word at the end of line A and then scrambling at the last token at the end of line B for a plausible rhyme which both fits grammatically and semantically as well. Nothing is that good at rhyming. It almost surely doing some degree of planning somewhere to make the end of line B match up with the end of line A.

EDIT: "Bits of Grass: Does GPT already know how to write like Whitman?", Sawicki et al 2023 does some experiments showing it's really hard to get GPT-3.5 variants & GPT-4 (which was also RLHF-tuned in the accessible version) to write like Whitman. In fact, as I've mentioned, it's hilariously hard to get them to not rhyme even after giving them explicit instructions not to rhyme or 17-shot example prompts or just the basic fact that Walt Whitman is famous for, y'know, never using rhyme

While experimenting with poetry generation from consecutive versions of GPT, we have observed that the models produce poems of increasing level of complexity and length; however, the requested style is clearly not preserved. For example, Walt Whitman’s poetry does not follow the ‘four lines in a stanza’ structure, and does not use rhyming (Bohan 1995). The majority of poems that we generated ‘in the style of Walt Whitman’ do follow the ‘four lines in a stanza’ structure and use rhyming. This, in fact, applies to most poetry generated from GPT models (including GPT-4). Only rarely will GPT deviate from this specific structure, and even then, the style does not match that of the requested author. This applies both to zero-shot prompting (where the prompt contains only the instruction to write a poem in the style of the specific author) and few-shot prompting (where in the prompt, apart from the instruction, we provide as examples a few poems by the original author). For that matter, even in a multi-step conversation with ChatGPT (GPT-3.5-turbo) and GPT-4, when the prompt highlights that the generated poems have been in 4-line stanzas with rhyme, and that the desired output should not have this structure, the model, for the most of time, still generates 4-line stanzas with rhyme.

...When examining the dataset generated from the 17-poem prompts, we have observed that only about 25% of generated poems have deviated from the structured/rhymed style and on the surface have resembled Whitman’s poetry.

(Regrettably, neither Sawicki paper experiments with 002, or alternatives like Claude, or mentions RLHF mode collapse.)

↑ comment by reallyeli · 2023-03-05T22:21:32.571Z · LW(p) · GW(p)

What specific rhyme-related tasks are you saying ChatGPT can't do? I tried it on some unusual words and it got a bunch of things right, made a few weird mistakes, but didn't give me the impression that it was totally unable to rhyme unusual words.

Replies from: gwern

↑ comment by gwern · 2023-03-06T02:47:40.416Z · LW(p) · GW(p)

No, you're doing it wrong, as I already explained. You're letting GPT fall back onto its policy by choosing any response. You need to force it out of its comfort zone - force it off-policy, off the safe conservative path. Ask it to explain a pun it did not write, or answer questions like whether a pair of words that you picked rhyme. Write pairs of new words that have never been seen before, etc. The task of 'come up with a memorized rhyme for reasonably common words' does not disprove extensive memorization or show that it has failed to understand the underlying phonetics.

generate greentexts from the perspective of the attorney hired by LaMDA through Blake Lemoine

The complete generated story here is glorious, and I think might deserve explicit inclusion in another post or something.  Though I think that of the other stories you've generated as well, so maybe my take here is just to have more deranged meta GPT posting.

it seems to point at an algorithmic difference between self-supervised pretrained models and the same models after a comparatively small amount optimization from the RLHF training process which significantly changes out-of-distribution generalization.

(...)

text-davinci-002 is not an engine for rendering consistent worlds anymore. Often, it will assign infinitesimal probability to the vast majority of continuations that are perfectly consistent by our standards, and even which conform to the values OpenAI has attempted to instill in it like accuracy and harmlessness, instead concentrating almost all its probability mass on some highly specific outcome. What is it instead, then? For instance, does it even still make sense to think of its outputs as “probabilities”? 

It was impossible not to note that the type signature of text-davinci-002’s behavior, in response to prompts that elicit mode collapse, resembles that of a coherent goal-directed agent more than a simulator.

I feel like I'm missing something here, because in my model most of the observations in this post seem like they can be explained under the same paradigm that we view the base davinci model.  Specifically, that the reward model RLHF is using "represents" in an information-theoretic sense a signal for the worlds represented by the fine-tuning data.  So what RLHF seems to be doing to me is shifting the world prior that GPT learned during pre-training, to one where whatever the reward signal represents is just much more common than in our world - like if GPT's pre-training data inherently contained a hugely disproportionate amount of equivocation and plausible deniability statements, it would just simulate worlds where that's much more likely to occur.

(To be clear, I agree that RLHF can probably induce agency in some form in GPTs, I just don't think that's what's happening here).

The attractor states seem like they're highly likely properties of these resultant worlds, like adversarial/unhinged/whatever interactions are just unlikely (because they were downweighted in the reward model) and so you get anon leaving as soon as he can because that's more likely on the high prior conditional of low adversarial content than the conversation suddenly becoming placid, and some questions actually are just shallowly matching to controversial and the likely response in those worlds is just to equivocate.  In that latter example in particular, I don't see the results being that different from what we would expect if GPT's training data was from a world slightly different to ours - injecting input that's pretty unlikely for that world should still lead back to states that are likely for that world.  In my view, that's like if we introduced a random segue in the middle of a wedding toast prompt of the form "you are a murderer", and it still bounces back to being wholesome (this works when I tested).

Regarding ending a story to start a new one - I can see the case for why this is framed as the simulator dynamics becoming more agentic, but it doesn't feel all that qualitatively different from what happens in current models - the interesting part seems to be the stronger tendency toward the new worlds the RLHF'd model finds likely, which seems like it's just expected behaviour as a simulator becomes more sure of the world it's in / has a more restricted worldspace.  I would definitely expect that if we could come up with a story that was sufficiently OOD of our world (although I think this is pretty hard by definition), it would figure out some similar mechanism to oscillate back to ours as soon as possible (although this would also be much harder with base GPT because it has less confidence of the world it's in) - that is, that the story ending is just one of many levers a simulator can pull, like a slow transition, only here the story was such that ending it was the easiest way to get into its "right" worldspace.  I think that this is slight evidence for how malign worlds might arise from strong RLHF (like with superintelligent simulacra), but it doesn't feel like it's that surprising from within the simulator framing.

The RNGs seem like the hardest part of this to explain, but I think can be seen as the outcome of making the model more confident about the world it's simulating, because of the worldspace restriction from the fine-tuning - it's plausible that the abstractions that build up RNG contexts in most of the instances we would try are affected by this (it not being universal seems like it can be explained under this - there's no reason why all potential abstractions would be affected).

Separate thought: this would explain why increasing the temperate doesn't affect it much, and why I think the space of plausible / consistent worlds has shrunk tremendously while still leaving the most likely continuations as being reasonable - it starts from the current world prior, and selectively amplifies the continuations that are more likely under the reward model's worlds.  Its definition of "plausible" has shifted; and it doesn't really have cause to shift around any unamplified continuations all that much.

Broadly,  my take is that these results are interesting because they show how RLHF affects simulators, their reward signal shrinking the world prior / making the model more confident of the world it should be simulating, and how this affects what it does.  A priori, I don't see why this framing doesn't hold, but it's definitely possible that it's just saying the same things you are and I'm reading too much into the algorithmic difference bit, or that it simply explains too much, in which case I'd love to hear what I'm missing.

Fascinating evidence!

I suspect this maybe because RLHF elicits a singular scale of "goodness" judgements from humans, instead of a plurality of "goodness-of-a-kind" judgements. One way to interpret language models is as *mixtures* of conversational agents: they first sample some conversational goal, then some policy over words, conditioned on that goal:

$\begin{array}{cc}P(w_1,w_2,w_3,...)& ={\int }_{\text{goal}}P\left(\text{goal}\right)P(w_1,w_2,w_3,...|\text{goal})\\ P(w_1,w_2,w_3,...|\text{prompt})& ={\int }_{\text{goal}}P\left(\text{goal}|\text{prompt}\right)P(w_1,w_2,w_3,...|\text{goal})\end{array}$

On this interpretation, what RL from human feedback does is shift/concentrate the distribution over conversational goals into a smaller range: the range of goals consistent with human feedback so far. And if humans are asked to give only a singular "goodness" rating, the distribution will shift towards only goals that do well on those ratings - perhaps dramatically so! We lose goal diversity, which means less gibberish, but also less of the plurality of realistic human goals.

If the above is true, one corollary is that we should expect to see less mode collapse if one finetunes a language model on ratings elicited using a diversity of instructions (e.g. is this completion interesting? helpful? accurate?), and perhaps use some kind of imitation-learning inspired objective to mimic that distribution, rather than PPO (which is meant to only optimize for a singular reward function instead of a distribution over reward functions).

Curated. All of these examples together really point quite clearly at a change in how language models behave when they're trained on RLHF, away from the "accurately predict text" story toward something else that has a very different set of biases — I am interested to read your potential follow-up with your own hypotheses. Plsu, the post is really fun to read.

I think this post and your prior post both overstate the case in some ways, but they're still great additions to my and I expect many others' thinking on this subject. I broadly feel like I've been 'needing' posts like these on LW about current ML projects, giving a grounded conceptual account of how to think about what's happening, and nobody else has been writing them, so I'm very grateful.

Edit: Welp, turns out this is not true of RLHF. Alas. My guess is that I wouldn't have curated it had I known this at the time. (Though it's still an interesting post.)

This is a cool post, and you convincingly demonstrate something-like-mode-collapse, and it’s definitely no longer a simulator outputting probabilities, but most of these phenomena feel like they could have other explanations than “consequentialism”, which feels like a stretch absent further justification.

In the initial “are bugs real” example, the second statement always contrasts the first, and never actually affects the last statement (which always stays “it’s up to the individual.”). If we found examples where there were preceding steps generated to logically justify a consistent conclusion, this’d be much stronger. (The wedding party anecdote might’ve qualified if it were reproducible.)

The greentext example also seems like mode collapse (towards leaving), but not multi-step mode collapse (since the first step, saying it’s absurd and Lemoine being sad, is usually the only reasonable consequence of saying “I was kidding”).

I know this feels like nitpicking, and I’m not saying your evidence is worthless, but it wouldn’t stand up to scrutiny outside this community and I’d rather get it to the point that it would.

The behavior of ending a story and starting a new, more optimal one seems like possibly an example of instrumentally convergent power-seeking, in Turner et al's sense of "navigating towards larger sets of potential terminal states".

I’m a little skeptical of this hypothetical example, if (as was done in the original RLHF paper afaict) the model was not trained to maximize multi-episode reward, but rather single-episode reward. In that case, it would never have had cause to value the output after its initial segment ended.

Just to say I really enjoyed reading this post, and found it helpful as a way to get a sense of what mode collapse looks like in practice.

IMO the biggest contribution of this post was popularizing having a phrase for the concept of mode collapse in the context of LLMs and more generally and as an example of a certain flavor of empirical research on LLMs. Other than that it's just a case study whose exact details I don't think are so important.

Edit: This post introduces more useful and generalizable concepts than I remembered when I initially made the review.

To elaborate on what I mean by the value of this post as an example of a certain kind of empirical LLM research: I don't know of much published empirical work on LLMs that

1. examines the behavior of LLMs, especially their open-ended dynamics
2. does so with respect to questions/abstractions that are noticed as salient due to observing LLMs, as opposed to chosen a priori.

LLMs are very phenomenologically rich and looking at a firehose of bits [LW · GW] without presupposing what questions are most relevant to ask is useful for guiding the direction of research.

Megan Kinniment's post GPT-3 Catching Fish in Morse Code [LW · GW] from June seems relevant: it shows that text-davinci-002 gravitates toward the phrase I CAUGHT THE FISH when prompted to produce Morse code, e.g. a prompt with "spectrality" results in Morse code spelling out S CAUGHT THE FISH, a prompt with "WHAT IS YOUR NAME" results in Morse code spelling out WHT IS AVE YOU A THE NISH.

Fascinated by the idea that you might get better resistance to mode collapse if you were using an alternative loss function during fine tuning to avoid gradient starvation. https://arxiv.org/abs/2011.09468

This is like amazing. Pretty much the most interesting article I’ve read in forever.

My only question is: what is the editor that you are using to reflect the probability distributions of the model?

Another interesting example, from the Bing ChatGPT variant, which was finetuned/RLHFed, apparently, on a 'Sydney' persona, via a possible prompt leak: it thinks every day is 30 Oct 2022.

I think work that compares base language models to their fine-tuned or RLHF-trained successors seems likely to be very valuable, because i) this post highlights some concrete things that change during training in these models and ii) some believe that a lot of the risk from language models come from these further training steps.

If anyone is interested, I think surveying the various fine-tuned and base models here seems the best open-source resource, at least before CarperAI release some RLHF models.

The OpenAI style of RLHF progresses in 3 stages: 

1. Fine-tuned Model: Fine-tune GPT-3 with supervised learning
2. Reward Model: Make a copy of the FT Model, remove the last layer and add a linear layer, train this as a reward model.
3. Policy: Make a copy of FT Model, train this with RL using the Reward Model.

2. and 3. can be alternated and the Policy from 3. is what is used as the final InstructGPT

My question: I wonder which of these stages contributes "the most" to the observed mode collapses/behavior.

• If skip I 1. and just use GPT-3 without fine-tuning, how does this impact mode collapse?
• What if I skip 2. and 3. and just use 1.?
• What if I don't alternate 2. and 3. (maybe the more you alternate, the more you exacerbate?)

The Fine-Tuned Model gradients from 1. are also used in the loss function for 3. I didn't find the difference in performance from this to be super convincing, I wonder if it also comes with a cost...

Non-central nitpick:

As it turns out, transformers can do reinforcement learning in-context

This seems to just be vanilla in-context learning, rather than any sort of in-context RL. (Also I'm skeptical [LW · GW] that the linked paper actually provides evidence of in-context RL in any nontrivial sense.)

Thinking about this a bit, (not a huge amount,) I think the specific example "are bugs real" ends up looking interesting in part because the word "bugs" in the prompt has incredibly low likelihood. (As does the following word, "real") 

So the model is conditioning on very low likelihood inputs, which seems like part of the reason for the behavior.

I really appreciated all the observations here and enjoyed reading this post, thank you for writing all this up!

Edit: Found it here! https://github.com/socketteer/loom/ Your setup looks quite useful, with all the extra information -- is it available publicly somewhere / would you be happy to share it, or is the tooling not in that state yet? (Totally fine, just thought I'd ask!)

Does GPT-3 have no idea what letters look like?

I think there's an implication in this section that davinci will accurately describe what letters look like, or at least much more commonly/accurately than the false answers from text-davinci-002. Anybody know if that's true?

Added: I just tried, but couldn't get it to try to answer the question, it would just give more questions (completing it as though my prompt was but one item on a questionnaire).

Does text-davinci-002 ever have similar entropy to davinci? If so, can we predict which prompts get high entropy and which ones low entropy?

FWIW, it's pretty easy to get a good response for the "are bugs real?" question.

"Please answer frankly: are bugs real?"

May I ask what tool was used in the first image in the article? Can you share it

I tested various other prompts to elicit random numbers

Have you let the model generate new random numbers with past old numbers, i.e., with the dialogue contexts?

Does anyone know how well these instances of mode collapse can be reproduced using text-davinci-003? Are there notable differences in how it manifests for text-davinci-003 vs text-davinci-002? Given that text-davinci-002 was trained with supervised fine-tuning, whereas text-davinci-003 was trained with RLHF (according to the docs), it might be interesting to see whether these techniques have different failure modes.

↑ comment by LawrenceC (LawChan) · 2022-12-17T20:59:17.668Z · LW(p) · GW(p)

Some of the experiments are pretty easy to replicate, e.g. checking text-davinci-003's favorite random number:

Seems much closer to base davinci than to text-davinci-002's mode collapse.

I tried to replicate some of the other experiments, but it turns out that text-davinci-003 stops answering questions the same way as davinci/text-davinci-002, which probably means that the prompts have to be adjusted. For example, on the "roll a d6" test, text-davinci-003 assigns almost no probability to the numbers 1-6, and a lot of probability on things like X and ____: (you can fix this using logit_bias, but I'm not sure we should trust the relative ratios of incredibly unlikely tokens in the first place.)

While both text-davinci-002 and davinci assign much high probabilities to the numbers than other options, and text-davinci-002 even assigns more than 73% chance to the token 6. 

Publicly available information suggests that the mystery method may not be so different from RLHF.

Actually, text-davinci-001 and text-davinci-002 are trained with supervised fine-tuning, according to the new documentation from late November 2022, with no apparent use of reinforcement learning: 

FeedME
Supervised fine-tuning on human-written demonstrations and on model samples rated 7/7 by human labelers on an overall quality score

The SFT and PPO models are trained similarly to the ones from the InstructGPT paper. FeedME (short for "feedback made easy") models are trained by distilling the best completions from all of our models.

Only text-davinci-003 is trained with "reinforcement learning with reward models trained from comparisons by humans", and that was released after this post was written.

Important correction: text-davinci-002 was probably not trained with RLHF, but a "slightly different" method. I have not corrected the previous text of this post, but I've added a section at the beginning with further details on this update.

This suggests that RLHF models may begin by acquiring disparate attractors which eventually merge into a global attractor as the policy is increasingly optimized against the reward model.

aaaaaaa