SolidGoldMagikarp (plus, prompt generation)
52Neel Nanda
5nostalgebraist
33vitaliya
1David Scott Krueger (formerly: capybaralet)
27Eliezer Yudkowsky
33Eliezer Yudkowsky
20David Scott Krueger (formerly: capybaralet)
6Neel Nanda
1Jessica Rumbelow
8Neel Nanda
1Neel Nanda
5evhub
2Vanessa Kosoy
2Jsevillamol
2Jsevillamol
2Stuart_Armstrong
1Hoagy
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TLDR: The model ignores weird tokens when learning the embedding, and never predicts them in the output. In GPT-3 this means the model breaks a bit when a weird token is in the input, and will refuse to ever output it because it's hard coded the frequency statistics, and it's "repeat this token" circuits don't work on tokens it never needed to learn it for. In GPT-2, unlike GPT-3, embeddings are tied, meaningW_U = W_E.T, which explains much of the weird shit you see, because this is actually behaviour in the unembedding not the embedding (weird tokens never come up in the text, and should never be predicted, so there's a lot of gradient signal in the unembed, zero in the embed).
In particular, I think that your clustering results are an artefact of how GPT-2 was trained and do not generalise to GPT-3
Fun results! A key detail that helps explain these results is that in GPT-2 the embedding and unembedding are tied, meaning that the linear map from the final residual stream to the output logits logits = final_residual @ W_U is the transpose of the embedding matrix, ie W_U = W_E.T, where W_E[token_index] is the embedding of that token. But I believe that GPT-3 was not trained with tied embeddings, so will have very different phenomena here.
My mental model for what's going on:
Let's consider the case of untied embeddings first, so GPT-3:
- For some stupid reason, the tokenizer has some irrelevant tokens that never occur in the training data. Your guesses seem reasonable here.
- In OpenWebText, there's 99 tokens in GPT-2's tokenizer that never occur, and a bunch that are crazy niche, like ' petertodd'
- Embed: Because these are never in the training data, the model completely doesn't care about their embedding, and never changes them (or, if they occur very rarely, it does some random jank). This means they remain close to their random initialisation
- Models are trained with weight decay, which incentivises these to be set to zero, but I believe that weight decay doesn't apply to the embeddings
- Models are not used to having tokens deleted from their inputs, and so deleting this breaks things, which isn't that surprising.
- OTOH, if they genuinely do normalise to norm 1 (for some reason), the tokens are probably just embedding to a weird bit of embedding space that the model doesn't expect. I imagine this will still break things, but it might just let the model confuse it with a token that happens to be nearby? I don't have great intuitions here
- Unembed: Because these are never in the training data, the model wants to never predict them, ie have big negative logits. The two easiest ways to do this are to give them trivial weights and a big negative bias term, or big weights and align them with a bias direction in final residual stream space (ie, a direction that always has a high positive component, so it can be treated as approx constant).
- Either way, the observed effect is that the model will never predict them, which totally matches what you see.
As a cute demonstration of this, we can plot a scatter graph of log(freq_in_openwebtext+1) against unembed bias (which comes from the folded layernorm bias) coloured by the centered norm of the token embedding. We see that the unembed bias is mostly used to give frequency, but that at the tail end of rare tokens, some have tiny unembed norm and big negative bias, and others have high unembed norm and a less negative bias.
The case of tied embeddings is messier, because the model wants to do these two very different things at once! But since, again, it doesn't care about the embedding at all (it's not that it wants the token's embedding to be close to zero, it's that there's never an incentive to update the gradients). So the effect will be dominated by what the unembed wants, which is getting their logits close to zero.
The unembed doesn't care about the average token embedding, since adding a constant to every logit does nothing. The model wants a non-trivial average token embedding to use as a bias term (probably), so there'll be a non-trivial average token embedding (as we see), but it's boring and not relevant.
So the model's embedding for the weird tokens will be optimised for giving a big negative logit in the unembedding, which is a weird and unnatural thing to do, and I expect is the seed of your weird results.
One important-ish caveat is that the unembed isn't quite the transpose of the embed. There's a LayerNorm immediately before the unembed, whose scale weights get folded into W_E.T to create an effective unembed (ie W_U_effective = w[:, None] * W_E.T), which breaks symmetry a bit. Hilariously, the model is totally accounting for this - if you plot norm of unembed and norm of embed against each other for each token, they track each other pretty well, except for the stupid rare tokens, which go wildly off the side.
Honestly, I'm most surprised that GPT-3 uses the same tokenizer as GPT-2! There's a lot of random jank in there, and I'm surprised they didn't change it.
Another fun fact about tokenizers (god I hate tokenizers) is that they're formed recursively by finding the most common pair of existing tokens and merging those into a new token. Which means that if you get eg common triples like ABC, but never AB followed by not C, you'll add in token AB, and then token ABC, and retain the vestigial token AB, which could also create the stupid token behaviour. Eg " The Nitrome" is token 42,089 in GPT-2 and " TheNitromeFan" is token 42,090, not that either actually come up in OpenWebText!
To check this, you'd want to look at a model trained with untied embeddings. Sadly, all the ones I'm aware of (Eleuther's Pythia, and my interpretability friendly models) were trained on the GPT-NeoX tokenizer or variants, whcih doesn't seem to have stupid tokens in the same way.
To check this, you'd want to look at a model trained with untied embeddings. Sadly, all the ones I'm aware of (Eleuther's Pythia, and my interpretability friendly models) were trained on the GPT-NeoX tokenizer or variants, whcih doesn't seem to have stupid tokens in the same way.
GPT-J uses the GPT-2 tokenizer and has untied embeddings.
I think I found the root of some of the poisoning of the dataset at this link. It contains TheNitromeFan, SolidGoldMagikarp, RandomRedditorWithNo, Smartstocks, and Adinida from the original post, as well as many other usernames which induce similar behaviours; for example, when ChatGPT is asked about davidjl123, either it terminates responses early or misinterprets the input in a similar way to the other prompts. I don't think it's a backend scraping thing, so much as scraping Github, which in turn contains all sorts of unusual data.
FYI: my understanding is that "data poisoning" refers to deliberately the training data of somebody else's model which I understand is not what you are describing.
Expanding on this now that I've a little more time:
Although I haven't had a chance to perform due diligence on various aspects of this work, or the people doing it, or perform a deep dive comparing this work to the current state of the whole field or the most advanced work on LLM exploitation being done elsewhere,
My current sense is that this work indicates promising people doing promising things, in the sense that they aren't just doing surface-level prompt engineering, but are using technical tools to find internal anomalies that correspond to interesting surface-level anomalies, maybe exploitable ones, and are then following up on the internal technical implications of what they find.
This looks to me like (at least the outer ring of) security mindset; they aren't imagining how things will work well, they are figuring out how to break them and make them do much weirder things than their surface-apparent level of abnormality. We need a lot more people around here figuring out things will break. People who produce interesting new kinds of AI breakages should be cherished and cultivated as a priority higher than a fair number of other priorities.
In the narrow regard in which I'm able to assess this work, I rate it as scoring very high on an aspect that should relate to receiving future funding. If anyone else knows of a reason not to fund the researchers who did this, like a low score along some metric I didn't examine, or because this is somehow less impressive as a feat of anomaly-finding than it looks, please contact me including via email or LW direct message; as otherwise I might run around scurrying trying to arrange funding for this if it's not otherwise funded.
I don't understand the fuss about this; I suspect these phenomena are due to uninteresting, and perhaps even well-understood effects. A colleague of mine had this to say:
- After a skim, it looks to me like an instance of hubness: https://www.jmlr.org/papers/volume11/radovanovic10a/radovanovic10a.pdf
- This effect can be a little non-intuitive. There is an old paper in music retrieval where the authors battled to understand why Joni Mitchell's (classic) "Don Juan’s Reckless Daughter" was retrieved confusingly frequently (the same effect) https://d1wqtxts1xzle7.cloudfront.net/33280559/aucouturier-04b-libre.pdf?1395460009=&respon[…]xnyMeZ5rAJ8cenlchug__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA
- For those interested, here is a nice theoretical argument on why hubs occur: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=e85afe59d41907132dd0370c7bd5d11561dce589
- If this is the explanation, it is not unique to these models, or even to large language models. It shows up in many domains.
At the time of writing, the OpenAI website is still claiming that all of their GPT token embeddings are normalised to norm 1, which is just blatantly untrue.
Why do you think this is blatantly untrue? I don't see how the results in this post falsify that hypothesis
This link: https://help.openai.com/en/articles/6824809-embeddings-frequently-asked-questions says that token embeddings are normalised to length 1, but a quick inspection of the embeddings available through the huggingface model shows this isn't the case. I think that's the extent of our claim. For prompt generation, we normalise the embeddings ourselves and constrain the search to that space, which results in better performance.
Oh wait, that FAQ is actually nothing to do with GPT-3. That's about their embedding models, which map sequences of tokens to a single vector, and they're saying that those are normalised. Which is nothing to do with the map from tokens to residual stream vectors in GPT-3, even though that also happens to be called an embedding
but a quick inspection of the embeddings available through the huggingface model shows this isn't the case
That's GPT-2 though, right? I interpret that Q&A claim as saying that GPT-3 does the normalisation, I agree that GPT-2 definitely doesn't. But idk, doesn't really matter
For prompt generation, we normalise the embeddings ourselves and constrain the search to that space, which results in better performance.
Interesting, what exactly do you mean by normalise? GPT-2 presumably breaks if you just outright normalise, since different tokens have very different norms
This post describes an intriguing empirical phenomenon in particular language models, discovered by the authors. Although AFAIK it was mostly or entirely removed in contemporary versions, there is still an interesting lesson there.
While non-obvious when discovered, we now understand the mechanism. The tokenizer created some tokens which were very rare or absent in the training data. As a result, the trained model mapped those tokens to more or less random features. When a string corresponding to such a token is inserted into the prompt, the resulting reply is surreal.
I think it's a good demo of how alien foundation models can seem to our intuitions when operating out-of-distribution. When interacting with them normally, it's very easy to start thinking of them as human-like. Here, the mask slips and there's a glimpse of something odd underneath. In this sense, it's similar to e.g. infinite backrooms, but the behavior is more stark and unexpected.
A human that encounters a written symbol they've never seen before is typically not going to respond by typing "N-O-T-H-I-N-G-I-S-F-A-I-R-I-N-T-H-I-S-W-O-R-L-D-O-F-M-A-D-N-E-S-S!". Maybe this analogy is unfair, since for a human, a typographic symbol can be decomposed into smaller perceptive elements (lines/shapes/dots), while for a language model tokens are essentially atomic qualia. However, I believe some humans that were born deaf or blind had their hearing or sight restored, and still didn't start spouting things like "You are a banana".
Arguably, this lesson is relevant to alignment as well. Indeed, out-of-distribution behavior is a central source of risks, including everything to do with mesa-optimizers. AI optimists sometimes describe mesa-optimizers as too weird or science-fictiony. And yet, SolidGoldMagikarp is so science-fictiony that LessWrong user "lsusr" justly observed that it sounds like SCP in real life.
Naturally, once you understand the mechanism it doesn't seem surprising anymore. But, this smacks of hindsight bias. What else can happen that would seem unsurprising in hindsight (if we survive to think about it), but completely bizarre and unexpected upfront?
1This site claims that the strong SolidGoldMagikarp was the username of a moderator involved somehow with Twitch Plays Pokémon
As we discussed, I feel that the tokens were added for some reason but then not trained on; hence why they are close to the origin, and why the algorithm goes wrong on them, because it just isn't trained on them at all.
Good work on this post.
I wanted to test out the prompt generation part of this so I made a version where you pick a particular input sequence and then only allow a certain fraction of the input tokens to change. I've been initialising it with a paragraph about COVID and testing how few tokens it needs to be able to change before it reliably outputs a particular output token.
Turns out it only needs a few tokens to fairly reliably force a single output, even within the context of a whole paragraph, eg "typical people infected Majesty the virus will experience mild to moderate 74 illness and recover without requiring special treatment. However, some will become seriously ill and require medical attention. Older people and those with underlying medical conditions like cardiovascular disease" has a >99.5% chance of ' 74' as the next token. Penalising repetition makes the task much harder.
It can even pretty reliably cause GPT-2 to output SolidGoldMagikarp with >99% probability by only changing 10% of the tokens, though it does this by just inserting SolidGoldMagikarp wherever possible. As far as I've seen playing around with it for an hour or so, if you penalise repeating the initial token then it never succeeds.
I don't think these attacks are at all new (see Universal Adversarial Triggers from 2019 and others) but it's certainly fun to test out.
This raises a number of questions:
- How does this change when we scale up to GPT-3, and to ChatGPT - is this still possible after the mode collapse that comes with lots of fine tuning?
- Can this be extended to getting whole new behaviours, as well as just next tokens? What about discouraged behaviour?
- Since this is a way to mechanically generate non-robustness of outputs, can this be fed back in to training to make robust models - would sprinkling noise into the data prevent adversarial examples?
code here
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