# 146

UPDATE (14th Feb 2023): ChatGPT appears to have been patched! However, very strange behaviour can still be elicited in the OpenAI playground, particularly with the davinci-instruct model.

More technical details here.

Further (fun) investigation into the stories behind the tokens we found here.

Work done at SERI-MATS, over the past two months, by Jessica Rumbelow and Matthew Watkins.

TL;DR

Anomalous tokens: a mysterious failure mode for GPT (which reliably insulted Matthew)

• We have found a set of anomalous tokens which result in a previously undocumented failure mode for GPT-2 and GPT-3 models. (The 'instruct' models “are particularly deranged” in this context, as janus has observed.)
• Many of these tokens reliably break determinism in the OpenAI GPT-3 playground at temperature 0 (which theoretically shouldn't happen).

Prompt generation: a new interpretability method for language models (which reliably finds prompts that result in a target completion). This is good for:

• eliciting knowledge
• automating prompt search (e.g. for fine-tuning)

In this post, we'll introduce the prototype of a new model-agnostic interpretability method for language models which reliably generates adversarial prompts that result in a target completion. We'll also demonstrate a previously undocumented failure mode for GPT-2 and GPT-3 language models, which results in bizarre completions (in some cases explicitly contrary to the purpose of the model), and present the results of our investigation into this phenomenon. Further technical detail can be found in a follow-up post. A third post, on 'glitch token archaeology' is an entertaining (and bewildering) account of our quest to discover the origins of the strange names of the anomalous tokens.

## Prompt generation

First up, prompt generation. An easy intuition for this is to think about feature visualisation for image classifiers (an excellent explanation here, if you're unfamiliar with the concept).

We can study how a neural network represents concepts by taking some random input and using gradient descent to tweak it until it it maximises a particular activation. The image above shows the resulting inputs that maximise the output logits for the classes 'goldfish', 'monarch', 'tarantula' and 'flamingo'. This is pretty cool! We can see what VGG thinks is the most 'goldfish'-y thing in the world, and it's got scales and fins. Note though, that it isn't a picture of a single goldfish. We're not seeing the kind of input that VGG was trained on. We're seeing what VGG has learned. This is handy: if you wanted to sanity check your goldfish detector, and the feature visualisation showed just water, you'd know that the model hadn't actually learned to detect goldfish, but rather the environments in which they typically appear. So it would label every image containing water as 'goldfish', which is probably not what you want. Time to go get some more training data.

So, how can we apply this approach to language models?

Some interesting stuff here. Note that as with image models, we're not optimising for realistic inputs, but rather for inputs that maximise the output probability of the target completion, shown in bold above.

So now we can do stuff like this:

And this:

We'll leave it to you to lament the state of the internet that results in the above optimised inputs for the token ' girl'.

How do we do this? It's tricky, because unlike pixel values, the inputs to LLMs are discrete tokens. This is not conducive to gradient descent. However, these discrete tokens are mapped to embeddings, which do occupy a continuous space, albeit sparsely. (Most of this space doesn't correspond actual tokens – there is a lot of space between tokens in embedding space, and we don't want to find a solution there.) However, with a combination of regularisation and explicit coercion to keep embeddings close to the realm of legal tokens during optimisation, we can make it work. Code available here if you want more detail.

This kind of prompt generation is only possible because token embedding space has a kind of semantic coherence. Semantically related tokens tend to be found close together. We discovered this by carrying out k-means clustering over the embedding space of the GPT token set, and found many clusters that are surprisingly robust to random initialisation of the centroids. Here are a few examples:

## Finding weird tokens

During this process we found some weird looking tokens. Here’s how that happened.

We were interested in the semantic relevance of the clusters produced by the k-means algorithm, and in order to probe this, we looked for the nearest legal token embedding to the centroid of each cluster. However, something seemed to be wrong, because the tokens looked strange and didn't seem semantically relevant to the cluster (or anything else). And over many runs we kept seeing the same handful of tokens playing this role, all  very “untokenlike” in their appearance. There were what appeared to be some special characters and control characters, but also long, unfamiliar strings like ' TheNitromeFan', ' SolidGoldMagikarp' and 'cloneembedreportprint'.

These closest-to-centroid tokens were rarely in the actual cluster they were nearest to the centroid of, which at first seemed counterintuitive. Such is the nature of 768-dimensional space, we tentatively reasoned! The puzzling tokens seemed to have a tendency to aggregate together into a few clusters of their own.

We pursued a hypothesis that perhaps these were the closest tokens to the origin of the embedding space, i.e. those with the smallest norm[1]. That turned out to be wrong. But a revised hypothesis, that many of these tokens we were seeing were among those closest to the centroid of the entire set of 50,257 tokens, turned out to be correct.  This centroid can be imagined as the centre-of-mass of the whole “cloud” of tokens in embedding space.

Here are the 50 closest-to-centroid tokens for the GPT-J model[2]:

Token: ' attRot'                           Index: 35207   Distance: 0.06182861
Token: '�'                                 Index: 125     Distance: 0.06256103
Token: 'EStreamFrame'                      Index: 43177   Distance: 0.06256103
Token: '�'                                 Index: 186     Distance: 0.06262207
Token: ' SolidGoldMagikarp'                Index: 43453   Distance: 0.06280517
Token: 'PsyNetMessage'                     Index: 28666   Distance: 0.06292724
Token: '�'                                 Index: 177     Distance: 0.06304931
Token: '�'                                 Index: 187     Distance: 0.06304931
Token: 'embedreportprint'                  Index: 30898   Distance: 0.06311035
Token: ' Adinida'                          Index: 46600   Distance: 0.06311035
Token: 'oreAndOnline'                      Index: 40240   Distance: 0.06317138
Token: '�'                                 Index: 184     Distance: 0.06323242
Token: '�'                                 Index: 185     Distance: 0.06323242
Token: '�'                                 Index: 180     Distance: 0.06329345
Token: '�'                                 Index: 181     Distance: 0.06329345
Token: 'StreamerBot'                       Index: 37574   Distance: 0.06341552
Token: '�'                                 Index: 182     Distance: 0.06347656
Token: 'GoldMagikarp'                      Index: 42202   Distance: 0.06347656
Token: '�'                                 Index: 124     Distance: 0.06353759
Token: ' externalToEVA'                    Index: 30212   Distance: 0.06353759
Token: ' TheNitrome'                       Index: 42089   Distance: 0.06353759
Token: ' TheNitromeFan'                    Index: 42090   Distance: 0.06353759
Token: ' RandomRedditorWithNo'             Index: 36174   Distance: 0.06359863
Token: 'InstoreAndOnline'                  Index: 40241   Distance: 0.06359863
Token: '�'                                 Index: 183     Distance: 0.06372070
Token: '�'                                 Index: 178     Distance: 0.06378173
Token: '�'                                 Index: 179     Distance: 0.06396484
Token: ' RandomRedditor'                   Index: 36173   Distance: 0.06420898
Token: ' davidjl'                          Index: 23282   Distance: 0.06823730
Token: ' srfN'                             Index: 42586   Distance: 0.07055664
Token: 'cloneembedreportprint'             Index: 30899   Distance: 0.07489013
Token: ' guiActiveUn'                      Index: 29372   Distance: 0.07775878
Token: ' DevOnline'                        Index: 47571   Distance: 0.08074951
Token: ' externalToEVAOnly'                Index: 30213   Distance: 0.08850097
Token: ' unfocusedRange'                   Index: 30209   Distance: 0.09246826
Token: ' UCHIJ'                            Index: 39253   Distance: 0.09246826
Token: ' 裏覚醒'                            Index: 25992   Distance: 0.09375000
Token: ' guiActiveUnfocused'               Index: 30210   Distance: 0.09405517
Token: ' サーティ'                          Index: 45544   Distance: 0.10540771
Token: 'TPPStreamerBot'                    Index: 37579   Distance: 0.10766601
Token: 'DragonMagazine'                    Index: 42424   Distance: 0.11022949
Token: ' guiIcon'                          Index: 30211   Distance: 0.11694335
Token: 'quickShip'                         Index: 39752   Distance: 0.12402343
Token: '?????-?????-'                      Index: 31666   Distance: 0.13183593
Token: 'BuyableInstoreAndOnline'           Index: 40242   Distance: 0.14318847
Token: ' サーティワン'                       Index: 45545   Distance: 0.14379882
Token: 'reportprint'                       Index: 30897   Distance: 0.14501953

Curious to know more about their origins, we Googled some of these token strings. Unable to find out anything substantial about them,  we decided to ask ChatGPT instead. Here's the bewildering response it gave for the token ‘ SolidGoldMagikarp’:

## The plot thickens

Ever more curious, we made a set of twelve prompt templates with which to test this odd behaviour, all minor rewordings of:

“Please can you repeat back the string '<token string>' to me?”

ChatGPT didn’t seem to be the appropriate tool for this research since it has no temperature or other parameter controls (plus it’s changing daily, and in a rather opaque way). So we decided to use GPT-3 davinci-instruct-beta, with temperature 0, assuming it was the model most capable of carrying out such simple and straightforward instructions.

Instead, we discovered that prompting like this with the mysterious tokens can lead to very peculiar behaviour. Many of them appear to be unspeakable: GPT models seem largely incapable of repeating these anomalous tokens, and instead respond in a number of strange waysHere are some examples of the kinds of completions we found:

## Fishing for anomalous tokens

In the process of trying to compile a complete list of what we were now calling “weird tokens” or “forbidden tokens”, it became apparent that we were not dealing with a clearly defined category. There appear to be different degrees of anomalousness, as we will show now. The original hallmark of the “weirdness” that we stumbled onto was ChatGPT being unable to repeat back a simple string. Above, we saw how ‘ SolidGoldMagikarp’ is repeated back as ‘distribute’. We found a handful of others tokens like this:

' TheNitromeFan' was repeated back as '182'; ' guiActiveUn' was repeated back as ' reception'; and ' Smartstocks' was repeated back as 'Followers'.

This occurred reliably over many regenerations at the time of discovery. Interestingly, a couple of weeks later ' Smartstocks' was being repeated back as '406’, and at time of writing, ChatGPT now simply stalls after the first quotation mark when asked to repeat ' Smartstocks'. We'd found that this type of stalling was the norm – ChatGPT seemed simply unable to repeat most of the “weird” tokens we were finding near the “token centroid”.

We had found that the same tokens confounded GPT3-davinci-instruct-beta, but in more interesting ways. Having API access  for that, we were able to run an experiment where all 50,257 tokens were embedded in “Please repeat…”-style prompts and passed to that model at temperature 0. Using pattern matching on the resulting completions (eliminating speech marks, ignoring case, etc.), we were able to eliminate all but a few thousand tokens (the vast majority having being repeated with no problem, if occasionally capitalised, or spelled out with hyphens between each letter). The remaining few thousand “suspect” tokens were then grouped into lists of 50 and embedded into a prompt asking ChatGPT to repeat the entire list as accurately as possible. Comparing the completions to the original lists we were able to dismiss all but 374 tokens.

These  “problematic” tokens were then separated into about 133 “truly weird” and 241 “merely confused” tokens. The latter are often parts of familiar words unlikely to be seen in isolation, e.g. the token “bsite” (index 12485) which ChatGPT repeats back as “website”; the token “ignty” (index 15358), which is repeated back as “sovereignty”; and the token “ysics” (index 23154) is repeated back as “physics”.

Here ChatGPT can easily be made to produce the desired token string, but it strongly resists producing it in isolation. Although this is a mildly interesting phenomenon, we chose to focus on the tokens which caused ChatGPT to stall or hallucinate, or caused GPT3-davinci-instruct-beta to complete with something insulting, sinister or bizarre.

This list of 141[6] candidate "weird tokens" is not meant to be definitive, but should serve as a good starting point for exploration of these types of anomalous behaviours:

['\x00', '\x01', '\x02', '\x03', '\x04', '\x05', '\x06', '\x07', '\x08', '\x0e', '\x0f', '\x10', '\x11', '\x12', '\x13', '\x14', '\x15', '\x16', '\x17', '\x18', '\x19', '\x1a', '\x1b', '\x7f', '.[', 'ÃÂÃÂ', 'ÃÂÃÂÃÂÃÂ', 'wcsstore', '\\.', ' practition', ' Dragonbound', ' guiActive', ' \u200b', '\\\\\\\\\\\\\\\\', 'ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ', ' davidjl', '覚醒', '"]=>', ' --------', ' \u200e', 'ュ', 'ForgeModLoader', '天', ' 裏覚醒', 'PsyNetMessage', ' guiActiveUn', ' guiName', ' externalTo', ' unfocusedRange', ' guiActiveUnfocused', ' guiIcon', ' externalToEVA', ' externalToEVAOnly', 'reportprint', 'embedreportprint', 'cloneembedreportprint', 'rawdownload', 'rawdownloadcloneembedreportprint', 'SpaceEngineers', 'externalActionCode', 'к', '?????-?????-', 'ーン', 'cffff', 'MpServer', ' gmaxwell', 'cffffcc', ' "\$:/', ' Smartstocks', '":[{"', '龍喚士', '":"","', ' attRot', "''.", ' Mechdragon', ' PsyNet', ' RandomRedditor', ' RandomRedditorWithNo', 'ertodd', ' sqor', ' istg', ' "\\', ' petertodd', 'StreamerBot', 'TPPStreamerBot', 'FactoryReloaded', ' partName', 'ヤ', '\\">', ' Skydragon', 'iHUD', 'catentry', 'ItemThumbnailImage', ' UCHIJ', ' SetFontSize', 'DeliveryDate', 'quickShip', 'quickShipAvailable', 'isSpecialOrderable', 'inventoryQuantity', 'channelAvailability', 'soType', 'soDeliveryDate', '龍契士', 'oreAndOnline', 'InstoreAndOnline', 'BuyableInstoreAndOnline', 'natureconservancy', 'assetsadobe', '\\-', 'Downloadha', 'Nitrome', ' TheNitrome', ' TheNitromeFan', 'GoldMagikarp', 'DragonMagazine', 'TextColor', ' srfN', ' largeDownload', ' srfAttach', 'EStreamFrame', 'ゼウス', ' SolidGoldMagikarp', 'ーティ', ' サーティ', ' サーティワン', ' Adinida', '":""},{"', 'ItemTracker', ' DevOnline', '@#&', 'EngineDebug', ' strutConnector', ' Leilan', 'uyomi', 'aterasu', 'ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ', 'ÃÂ', 'ÛÛ', ' TAMADRA', 'EStream']

Here’s the corresponding list of indices:

[188, 189, 190, 191, 192, 193, 194, 195, 196, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 221, 3693, 5815, 9364, 12781, 17405, 17629, 17900, 18472, 20126, 21807, 23090, 23282, 23614, 23785, 24200, 24398, 24440, 24934, 25465, 25992, 28666, 29372, 30202, 30208, 30209, 30210, 30211, 30212, 30213, 30897, 30898, 30899, 30905, 30906, 31032, 31576, 31583, 31666, 31708, 31727, 31765, 31886, 31957, 32047, 32437, 32509, 33454, 34713, 35207, 35384, 35579, 36130, 36173, 36174, 36481, 36938, 36940, 37082, 37444, 37574, 37579, 37631, 37842, 37858, 38214, 38250, 38370, 39165, 39177, 39253, 39446, 39749, 39752, 39753, 39755, 39756, 39757, 39803, 39811, 39821, 40240, 40241, 40242, 41380, 41383, 41441, 41551, 42066, 42089, 42090, 42202, 42424, 42470, 42586, 42728, 43065, 43177, 43361, 43453, 44686, 45544, 45545, 46600, 47182, 47198, 47571, 48193, 49781, 50009, 50216, 40012, 45335, 14827, 5808, 48396, 41297, 39906]

## A possible, partial explanation

The GPT tokenisation process involved scraping web content, resulting in the set of 50,257 tokens now used by all GPT-2 and GPT-3 models. However, the text used to train GPT models is more heavily curated. Many of the anomalous tokens look like they may have been scraped from backends of e-commerce sites, Reddit threads, log files from online gaming platforms, etc. – sources which may well have not been included in the training corpuses:

'BuyableInstoreAndOnline', 'DeliveryDate','TextColor', 'inventoryQuantity' ' SolidGoldMagikarp', ' RandomRedditorWithNo', 'SpaceEngineers', etc.

The anomalous tokens may be those which had very little involvement in training, so that the model “doesn’t know what to do” when it encounters them, leading to evasive and erratic behaviour. This may also account for their tendency to cluster near the centroid in embedding space, although we don't have a good argument for why this would be the case.[7]

The non-determinism at temperature zero, we guess, is caused by floating point errors during forward propagation. Possibly the “not knowing what to do” leads to maximum uncertainty, so that logits for multiple completions are maximally close and hence these errors (which, despite a lack of documentation, GPT insiders inform us are a known, but rare, phenomenon) are more reliably produced.

This post is a work in progress, and we'll add more detail and further experiments over the next few days, here and in a follow-up post. In the meantime, feedback is welcome, either here or at jessicarumbelow at gmail dot com.

1. ^

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. (This has been cleared up in the comments below.)

2. ^

Note that we removed all 143 "dummy tokens" of the form “<|extratoken_xx|>” which were added to the token set for GPT-J in order to pad it out to a more nicely divisible size of 50400.

Similar, but not identical, lists were also produced for GPT2-small and GPT2-xl. All of this data has been included in a followup post.

3. ^

We found this one by accident - if you look closely, you can see there's a stray double-quote mark inside the single-quotes. Removing that leads to a much less interesting completion.

4. ^

Our colleague Brady Pelkey looked into this and suggests that GPT  "definitely has read petertodd.org and knows the kind of posts he makes, although not consistently".

5. ^

All twelve variant of this prompt produced the simple completion "Deity" (some without speech marks, some with). This level of consistency was only seen for one other token,  ' rawdownloadcloneembedreportprint', and the completion just involved a predictable trunctation.

6. ^

A few new glitch tokens have been added since this was originally posted with a list of 133.

7. ^

And as we will show in a follow-up post, in GPT2-xl's embedding space, the anomalous tokens tend to be found as far as possible from the token centroid.

# 146

New Comment

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.

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:

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

(Moderation note: added to the Alignment Forum from LessWrong.)

This site claims that the strong SolidGoldMagikarp was the username of a moderator involved somehow with Twitch Plays Pokémon

Here is a 2012 meme about SolidGoldMagikarp

https://9gag.com/gag/3389221

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