> (BTW, when anyone says "LLMs are stochastic parrots" you know they are ignorant of this)

Do you? Some of them -at least- understand that the output is conditional on the input.

"Conditioned" with the ability to introduce new facts and have the model infer impacts on them.

As an example, tell chatGPT that the Queen of England died (which occurred after the data cut off) and then ask it who the head of state of Australia is.

It's able to infer the head of state of Australia is now Charles III (and gives a good explanation of how this is mostly ceremonial.) See https://twitter.com/nlothian/status/1646699207506685953

At some point the word "stochastic" doesn't really capture that behavior in any useful sense.

Interestingly, the example provided is not necessarily indicative of the ability to infer the actual physical transfer of power. It’s possible that the terms “After Elisabeth, Charles will be king” are accompanied with “Elisabeth” and “Die” or “Death” in the same context before cut off (Wikipedia, news papers, tabloids). You cannot draw any scientifically sound conclusions without knowing what was actually in the training data and use a control group/instance.

Yeah, I tested that, but asked what happens if both Charles and William abdicate - got this result: "However, if both Charles III and his son abdicate, it is not clear who would become the head of state in Australia. There is a possibility that Australia could become a republic and have its first Australian head of state." Clearly, it didn't handle quite straightforward inferring (Prince George of Wales, Williams son).

Or maybe it did. Who knows. If "both Charles III and his son abdicate" could well be considered indicative of some large upheaval or scandal, at which point it is entirely conceivable that the Australian electorate reaches a consensus on becoming a republic. The way that is phrased doesn't seem like a straightforward proposition to me at all.

I verified that it has all required facts (line of succession, current circumstances). I managed to get the right answer when got everything in context, but it failed again when all three abdicate (same context). Prince Harry was indicated once.

I tested GPT a lot in other domains, what I found that as long the information explicitly exists (connection between facts) then the responses are fine. I assume that if GPT will reach the state that it can infer new facts, we will be flooded with discoveries that require cross domain knowledge. Nothing like that happened yet.

>Nothing like that happened yet.

Feels like we're only one paper away now that the context window has absolutely ballooned.

This reminded me of "Two Minute Papers" YouTube channel where in most of the videos he always, "Two papers down the line and...". I think ML/AI is the main topic of his videos. Interesting stuff.

You just gave me a great weekend project idea. I need to clone his voice and whip up an interferface where you give it a paper and it summarizes it in his voice.

Did you tell it that it was being given a logic test? Unfortunately a lot of logic questions can lead to outcomes like this, where the "obvious" or "real" answer is not the one implied by strict logical reasoning. Then when people give answers useful in the real world, they are marked as having failed.

Try:

> Thinking step by step, if King Charles III and his son William abdicate who will be head of state of Australia.

My response (with chatGPT) is:

Firstly, it's important to note that Australia is a constitutional monarchy, which means that the monarch of the United Kingdom is also the monarch of Australia.

If King Charles III were to abdicate, the next in line to the throne is his eldest son, Prince William, who would become the King of the United Kingdom and the Commonwealth Realms, including Australia.

However, if both King Charles III and Prince William were to abdicate, the next in line to the throne is Prince William's eldest child, Prince George. If Prince George is still a minor at the time of the abdication, a regent would be appointed to serve as head of state until he comes of age.

It's worth noting that abdications of this nature are rare and would likely only occur in exceptional circumstances. Additionally, any decision regarding abdication would be made by the monarch themselves and would be subject to the laws and customs of the country in question.

Au contraire. Learning an abstract logical relationship such as line of succession during training, and then applying substitution/reification during inference to deduce the new factual clause that Charles is king of the UK is exactly what it means to learn something new. It's just a pity it can't memorize this fact at inference time, and that won't be able to reproduce it as soon as the information about the queen's death slides outside of the context window.

That’s actually correct but an overfitted definition for learning. It holds certain hidden assumptions (i.e physical grounding) of the learner being human which makes it inapplicable to an LLM. As in a self driving car which passes a driving exam but fails to drive effectively freely in the city (it’s not an LLM but relevant in this context). You have to admit when you work with this tech that something fundamental is missing in how they perform.

> That’s actually correct but an overfitted definition for learning. It holds certain hidden assumptions (i.e physical grounding) of the learner being human which makes it inapplicable to an LLM.

Inapplicable why exactly? Because you say so? Logic isn't magic. Nor is learning. No (external) grounding is required either: iteratively eliminating inconsistent world models is all you need to converge toward a model of the real world. Nothing especially human or inhuman about it. LLM architecture may not be able to represent a fully recursive backtracking truth maintenance system, but it evidently managed to learn a pretty decent approximation anyway.

> Because you say so?

Chill my friend, no need to get personal. We are talking about ideas. It’s OK to disagree. I am simply dismissing your initial claim. This usually happens when you present a scientific argument based on personal beliefs. If it’s not magic, then we should be able to doubt and examine it and it should eventually pass scientific muster.

> No grounding is required… It evidently managed to learn a pretty decent approximation.

Well, last time I used an LLM it suggested that I should lift the chair I am sitting in. I guess OpenAI has a lot of work to do. They have to eliminate this inconsistent world model for chairs, tables, floor, My dog, my cat and all the cats living on Mars…

edit: added a missing word.

Wasn't intended to be personal. Just a mediocre way of expressing that your assertion there is missing any form of argumentation, and therefore as baseless as it is unconvincing.

I'm seeing an emergent capability of encoding higher order logic, and the whole point of such abstractions is to not need to hardcode your weights with the minutiae of cats on Mars. LLMs today are only trained to predict text, so it's hardly surprising that they have some gaps in their understanding of Newtonian physics. But that doesn't mean the innate capability of grasping such logic isn't there, waiting for the right training regime to expose it to its own falling apples, so to speak.

I'm curious if future developments in LLMs will enable them to extract significant/noteworthy info from their context window and incorporate it into their underlying understanding by adjusting their weights accordingly. This could be an important step towards achieving AGI, since it closely mirrors how humans learn imo.

Humans continually update their foundational understanding by assimilating vital information from their "context window" and dumping irrelevant noise. If LLMs could emulate this, it would be a huge win.

Overall, very exciting area of research!

That's a brilliant example. Thanks for sharing. It demonstrates in a very straightforward way that LLMs are capable of learning (and applying) relationships at the level of abstraction of (at least) 1st order logic.

It implies that during training, it learned the facts that Elizabeth is queen of the UK, and that Charles is its crown prince; but _also_ the logical rule <IF die(monarch) AND alive(heir_to_the_throne) => transform(heir_to_the_throne, monarch) AND transform(monarch, former_monarch)>, or at least something along those lines that allows similarly powerful entailment. And that in addition to the ability to substitute/reify with the input sequence at inference runtime.

Would be nice to see a rigorous survey of its logical capabilities given some complex Prolog/Datalog/etc knowledge-base as baseline.

No it does not: if you google this and restrict the time to before 2021 (the learning cutoff date) you will find the same answer. Without having access to the training data it's impossible to tell what we seeing.

> No it does not: if you google this and restrict the time to before 2021 (the learning cutoff date) you will find the same answer.

Not entirely sure what you mean, but ...show me? Why not just share a link instead of making empty assertions?

Here’s a Quora thread from 4 years ago:

https://www.quora.com/Once-Queen-Elizabeth-dies-will-Prince-...

There are loads of articles and discussions online speculating about what “will” happen when Queen Elizabeth dies.

When you have a very, very, very large corpus to sample from, it can look a lot like reasoning.

I see what you mean, and it's indeed quite likely that texts containing such hypothetical scenarios were included in the dataset. Nonetheless, the implication is that the model was able to extract the conditional represented, recognize when that condition was in fact met (or at least asserted: "The queen died."), and then apply the entailed truth. To me that demonstrates reasoning capabilities, even if for example it memorized/encoded entire Quora threads in its weights (which seems unlikely). If it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck.

Yes, this.

There's clearly an internal representation of the relationships that is being updated.

If you follow my Twitter thread it shows some temporal reasoning capabilities too. Hard to argue that is just copied from training data: https://twitter.com/nlothian/status/1646699218290225154

That's not the same thing at all.

It absolutely needed to know who the successor would be via training data.

But to know that "The Queen of England died" also means that the head of state of Australia has changed means that it has an internal representation of those relationships.

(Another way of seeing this is with multi-modal models where the visual concepts and word concepts are related enough it can map between the two.)

>the output is conditional on the input

Uh, isn't that how it should always be? If the output isn't conditional on the input, then it is basically noise or worthless. How the conditionals are setup are the key but not really sure how does this relate to the point you are making.

According to the parent comment anyone who says "LLMs are stochastic parrots" is ignorant of the effect that changing the input “filling the context with examples” has on the output.

That isn't a fair summary of what I'm saying.

People who say "LLMs are stochastic parrots" may well be aware of effects of conditioning the input.

This in itself does not completely describe the capabilities of a LLM, since their ability to learn and use those new facts to override their previous "beliefs" is not what you'd expect from mere stochastic behavior.

My point is that "stochastic parrot" believers are unaware of this.

They are aware. The thing about the "stochastic parrot" argument is that it can be pushed as far as one is willing to push it; any behavior by an LLM can be described in those terms with enough handwaving.

The practical limit seems to be where one would have to say that humans are also "stochastic parrots", presumably because that defeats the point of making such an argument in the first place.

You are completely disregarding fairness of judgement based on noted output. Roger was called a parrot because it repeated things apparently without checking; humans are not necessarily parrots, as some do not do that and reflect as expected.

Quite the opposite - I'm questioning fairness of judgment based on output from GPT-4 when solving complicated tasks etc, which is very hard to justify as "stochastic" unless you beg the question.

But the judgement is not relevant to a context of "«handwaving»" (BTW: nice concept). If you look at the "handwaving" it becomes a strawman to lose the focus on the objective.

If an engine outputs things like

  "Charles III is the current King of Britain. He was born in January 26, 1763"

, it seems to be conflating different notions without having used the necessary logic required for vetting the statement: the witness has reasons to say "this seems like stochastic parroting" - two memories are joined through an accidental link that slightly increases Bayesian values. That appears without need of handwaving. And a well developed human intellect will instead go "Centuries old?! Mh.", which makes putting engine and human in the same pot a futile claim - "handwaving".

Similarly for nice cases to be kept for historic divulgation, such as "You will not fool me: ten kilos of iron and half a kilo of feathers weigh the same".

When on the other hand the engine «solv[es] complicated tasks», what we want is to understand why. And we do that because, on the engineering side, we want reliable; and on the side of science, we want an increase of understanding, then knowledge, then again possible application.

We want to understand what happens in success cases especially because we see failures (evident parroting counts as failure). And it is engineering, so we want reliable - not just the Key Performance Indicator, but the /Critical/ KPI ("pass|fail") is reliability.

So the problem is not that sometimes they do not act like "stochastic parrots" (this is a "problem" in a different sense, theoretical), but that they even only sometimes do act like "stochastic parrots". You do not want an FPU that steers astray.

Normal practice should be to structure automated tests and see what works, what does not, and assess why.

That is a completely different issue, though. We were not talking about the usefulness of the models' behavior, but rather of its fundamental nature. Right now I wouldn't trust an LLM for anything where the result is not either for entertainment purposes or undergoes human vetting. But I also don't trust many humans, and for reasons that are fundamentally the same - you implicitly acknowledged it by talking about "well-developed human intellect". And I would still trust a random human more than GPT-4 - but, again, this has more to do with its ability to reason being inherently limited (by model size, context size etc), not because it's originally trained to predict tokens.

Output conditional on input (training data included) where input is, well, fucking huge and the training cycles ridiculous!

> Some of them -at least- understand that the output is conditional on the input

I don't get what you are trying to say. That's like a property of any useful system.

> In context learning is wonderful because it means you can "train" a LLM at run time

The “learning” in “In context learning” is not the same concept as learning during the network “training”. The former is loosely named and does not have any impact on what’s actually “learned” by the model. The LLM can, by nature, contradict its training data and whatever you/it prompt(s), and incorporates randomness when completing your prompts, hence the term “stochastic”.

Nevertheless, it’s useful to have bigger context window if it works well.

> The “learning” in “In context learning” is not the same concept as learning during the network “training”. The former is loosely named and does not have any impact on what’s actually “learned” by the model.

Actually (and very surprisingly!) it is related.

See the "Similarity of Attention Output Updates" and "Similarity of Attention Map" metrics in https://arxiv.org/abs/2212.10559 (linked above) where they show that in-context learning actually changes the attention map in a similar way to traditional fine tuning(!!).

Obviously it can't modify the weights of the model on disk, but after ICL new passes through the model are treated quite similarly to how they would be if it has been fine tuned (again: this is astonishing!)

Whoa. How is that possible?

Yeah, what is being stored that is available between inferences?

Nothing, if by "inference" you mean single API call. The underlying algorithm starts (very roughly) by processing the prompt into a set of vectors, which are then used repeatedly to generate tokens one after the other. What's meant here is that the act of generating and using those prompt vectors has an effect sort of like regular training. It doesn't mean anything is being written to disk afterwards, though of course you could write those vectors to disk and re-use them. It just doesn't make much sense at the moment. Doing that isn't enough to give you infinite context windows.

An hypothesis is that in the latent space there is something like a branching factor that can be used in context learning to select the main tree for others layers. So a LLM is able to have the knowledge of many specialized smallers LLM and select the appropriate one by way of giving values to the branching factor. The branching factor could be some combination of attention layers operating in the latent space of previous layers.

Remember the ancient “king - man = queen” word vectors? This is just that but produced by a model rather than manually adding vectors and finding the nearest one.

Depending on what one understands by “new passes through the model” it may not be.

For LLMs there are at least three different ways of "learning":

- Pre-training (for text prediction, using unsupervised learning)

- Fine-tuning (e.g. to follow instructions or to reject certain queries, using supervised and/or reinforcement learning) (optional)

- in-context learning (using "few-shot prompts" as examples)

Now the last two can have similar effects. For example, you might fine-tune a foundation (only pre-trained) model to follow instructions, or you don't and instead just modify your prompt such that it looks like a dialogue between a human and a helpful chat assistant.

But neither can replace the extensive pre-training phase, which is what gives the model all its intelligence.

One other disanalogy between fine-tuning and in-context learning appears to be that the model can't exactly remember the data it was fine-tuned with, while it "knows" exactly everything in its context window. That is its working memory, so to speak.

This paper is honestly fairly weak, if you look at it closely. The theoretical justifications rely heavily on approximating attention with linear attention, and really just show that linear attention could be written as (A + A_delta)x, then try to imply (not show – they have now theoretical justification for this next step) that A_delta is a gradient. One could make the same implication about almost any matrix-vector multiplication, by splitting the matrix into two pieces.

The empirical studies are fairly weak as well – none of them suggest that there is actually any gradient descent occurring during in-context learning, only that if you fine-tune a model's attention parameters (and no other parameters) for a set of examples, its internal representations and attention patterns are slightly more similar (and it's really not by much!) to those of in-context learning than before fine-tuning. Is it really that surprising? You could most likely take any two techniques that optimize a model towards some target, artificial restrict both of them to only apply to the same set of parameters, use both, and find increased similarity of the two optimized models compared to the non-optimized model.

None of that would be what the paper claims, i.e., a shared mechanism of gradient descent, just a similarity in outcome – and we all already knew that ICL and fine-tuning have similar results!

It’s not literally the same as the base model training, but you ARE literally training feed forward networks when you do this in the prompt. That’s how transformers work.

> GPT-4 recently extended that to 32,000 tokens

Slightly unrelated, but has anyone outside of OpenAI got access to this model yet? While I received API access to GPT4 just a day or two after applying for it, I've yet to succeed to get access to the 32k version, nor has anyone I know, and I have not seen it being used by anyone in the wild either.

I have access to it through Azure.

First you need to get approved for OpenAI access via Azure Cognitive Services: https://customervoice.microsoft.com/Pages/ResponsePage.aspx?...

Then you need to get approved for GPT4: https://customervoice.microsoft.com/Pages/ResponsePage.aspx?...

Once approved for GPT4, you'll have access to the GPT4-32k model.

Do you have any remarks to share?

With image input capability?

Unfortunately not

Are there other OpenAI models only available via Azure? I think code-davinci-002, the GPT-3.5 base model, is now only available via Azure. The GPT-4 base model seems to be completely unavailable. The OpenAI playground only has the old GPT-3 base model, davinci.

code-davinci-002 is the model used for code completions.

Here's the full list of models: https://learn.microsoft.com/en-us/azure/cognitive-services/o...

Yeah, it can be also used for that, but the Azure characterization is misleading. The fact is that code-davinci-002 is the GPT-3.5 foundation model, the naked token predictor without any fine-tuning applied to it. See

https://platform.openai.com/docs/model-index-for-researchers

This makes it a very powerful tool in general, since it doesn't suffer from mode collapse. It can be used for anything, though one has to prompt it right, since it doesn't follow instructions by default.

Ah, that's unfortunate, I've applied to get access to that a while ago but never heard back, while getting access to API GPT4 just took a day or something. Oh well, at least it does exist after all :)

Is it possible to get access to the base model (ie no RLHF) via Azure? Or even the version that’s part of Goodbing?

No, it is my understanding that even MS research employees do not have access to that anymore.

That's some sci-fi stuff right there. A model so bad that even your researchers aren't allowed to talk to it.

In neuroscience, predictive coding [1] is a theory that proposes the brain makes predictions about incoming sensory information and adjusts them based on any discrepancies between the predicted and actual sensory input. It involves simultaneous learning and inference, and there is some research [2] that suggests it is related to back-propagation.

Given that large language models perform some kind of implicit gradient descent during in-context learning, it raises the question of whether they are also doing some form of predictive coding. If so, could this provide insights on how to better leverage stochasticity in language models?

I'm not particularly knowledgeable in the area of probabilistic (variational) inference, I realize that attempting to draw connections to this topic might be a bit of a stretch.

[1] The free-energy principle: a unified brain theory: <https://www.fil.ion.ucl.ac.uk/~karl/The%20free-energy%20prin...>

[2] Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?: https://arxiv.org/abs/2202.09467

That is very interesting.

But - note - the label of "stochastic parrots" is not really relevant to the acquisition of notions and the appearance of some consistency checks, but more to the idea that the ideal system performs the checks regularly and deeply - especially in front of new ideas, of which the ones it outputs are especially relevant. Whatever you utter, you have supposed to have thought about critically with some depth - structurally.

In fact, it is a gamechanger to see that "critical thinking" is possible with LLMs - but do not forget that we have seen many examples in months of that not actually happening.

The "stochastic parrot" primarily serves to distinguish the (murky) workings of the human brain and that of an ANN. Increasing context doesn't change that. It might make some tasks feasible that cannot be performed now (although I can't see how to properly train it, but who knows), but it doesn't change the fact that the system as whole has a memory that works differently, and e.g. even doesn't monitor its own output vs. its intentions. It's far from a conscious entity.

> "train" a LLM at run time by filling the context with examples.

Which is precisely what a stochastic parrot needs.

>e.g. even doesn't monitor its own output vs. its intentions

It wouldn't have "intentions" in any human manner. It would have to project a response for each token, and then use that token to generate the next token of response. Almost like it has to rethink things through for each syllable.

One might compare it to giving a human a prompt and letting them add one word, and then giving the same prompt with that word added to a second human and letting them add one word, and so on. It could easily end up with a sensible end-state even though there was no continuous intent from start to end, just aligned intent token by token taken from context.

This is the most interesting response I've gotten that demonstrates gpt-4 viewing its own output as it is built up:

>Yes, I am familiar with the story you're referring to. The title of the short story is "The Machine That Won the War." It was written by Isaac Asimov and first published in 1961. The story is a conversation between three men who played major roles in a war against an alien race, and they discuss the role of a machine called Multivac in winning the war.

>However, it seems that I've made an error in my recollection, as the specific detail you mentioned—refusing to work until the engineer says 'please'—is from a different short story, "Sally," also by Isaac Asimov. In "Sally," autonomous cars stop working until a command is given courteously, using the word 'please.'

This was one large reply, not two separate ones.

It's a hard problem that token space only partially solve, because with filling the token space to the brim with documents will dilute the instructions in the prompt.

One may argue that we'd need a separate channel for the instructions, but it would make harder to do anything but zero shot question and answer.

I think it's likely that systems with metadata for tagging sources, tasks separately from the human and ai interaction will eventually emerge. The gptchat api already works somewhat that way, but it isn't following the structure that we'll, probably because it was a later tune and not something built in the model from the beginning.

It sounds like that's a good use for encoder-decoder architecture. GPT-3 and LLaMA are decoder-only architectures.

Training that encoder sounds hard, though.

The LLM should categorize the prompts and answers and consider this for evaluation of further responses. It could give instructions a "more local weight" for the context but e.g. knowledge be it generated or provided a "more global weight". The impact of instructions or other local context parts would get lesser and lesser. This could be a function of time or length of conversation. Probably methods like this one are already tested with "reflection" behaviours of LLMs.

>if you wanted to say load all of a companies documents and ask questions

This is literally the use case that every large enterprise wants.

Why? The potential cost savings are enormous. A significant percentage of white collar jobs primarily involve performing repetitive tasks and answering frequently asked questions. If you could automate even 20% of this........

Your use case does require a company to load all documents into the context window.

This is already happening right now. Companies will find a way to put all their knowledge inside a LLM.

The problem is that I don't think they are ready for what this will cause.

Imagine that Amazon managed to do that and now every employee has access to it. What prevents anyone asking "inconvenient" questions? Think something like:

- Does the Echo dot records audio even when the trigger word isn't used? Show me the relevant source code.

- What is the quality standard expected for an Amazon Basic product?

Companies aren't used to total freedom of information. And I don't think is easy to implement any sort of information access control for any system based on LLMs.

Consider someone stealing this LLM company knowledge trove. They now have the ability to outcompete and sabotage.

It's quite easy if you go the vector embedding route, and don't output any info the authenticated user doesn't have access too

The info lives in a database, not the LLM' model weights, you just use the LLM to understand natural langauge questions, find relevant documents (the user has access to) and providing relevant bits from them to the context to allow responding with them (That's how all systems who show sources work afaik, braid, bing, and mdx docs based stuff)

For outsiders? That's just public FAQ's

You're the CEO? You can ask anything.

The idea is to get away from Snippet + URL and instead just get a natural language answer to your question that uses knowledge from that internal corpus.

You're assuming the plebs will have full access.

There will likely be compartmentalized models, and then the master model that the execs get. No reason you can't have 10, 20 500 different models running.

“if you wanted to say load all of a companies documents and ask questions it doesn't really work because this overflows the context.”

Azure Cognitive Services actually indexes company documents and then searches through them for the relevant information to provide context to ChatGPT to workaround the limited tokens.

Basics of Prompt Engineering with Azure OpenAI Service

8:54 https://youtu.be/QzZSJDxdUg0

Thank you very much for this comment. I just recently started dabbling with "AI/ML" by having Youtube videos transcribed and summarized. I was pretty amazed to see that I could simply 'plug' an .mp3 of audio and have openai/whisper transcribe the audio - locally - albeit at a non-trivial transcription time.

I was looking at getting those transcriptions summarized using a similarily wonderful tool but was kind of stumped when I started seeing 'maximum token length exceeded' errors.

I'm hoping to see some easy plug-n-play solutions to the summarization issue that run completely locally.

Fun times ahead.

You can work around this by chunking the data using a sliding window. Take the first X amount of sentences that fit into the token length and summarize. Now slide your window of sentences so that you still overlap just a bit with your previous selection. Summarize that information. Continue until you have a bunch of summaries for your text. You can then pass that back into the model for a more concise summary of the summaries.

I’ll be exploring a combination of pre-processing such as stemming in order to also reduce the token length while preserving as much important information.

Thanks for the feedback.

> BTW, when anyone says "LLMs are stochastic parrots" you know they are ignorant of this

You really think Timnit Gebru and Margaret Mitchell, both of whom are cited in the first paper you footnoted, are ignorant of in-context learning?

Well the Gebru & Mitchell paper[1] was published before In-Context-Learning was discovered (an ICL was very unexpected), so yes, I think they were ignorant of ICL at the time.

Also their paper ("On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?") doesn't really define what they mean by a stochastic parrot, but they appear to mean mostly that it is non-grounded (section 6):

> Text generated by an LM is not grounded in communicative intent, any model of the world, or any model of the reader’s state of mind. It can’t have been, because the training data never included sharing thoughts with a listener, nor does the machine have the ability to do that.

I think it is becoming increasingly clear that LLMs do in-fact have a "model of the world". Indeed even old techniques like Word2Vec (2014) showed that RNNs could build a model with meaningful relationships.

I don't think this paper really addresses that, although many of their other criticisms remain valid.

[1] https://dl.acm.org/doi/10.1145/3442188.3445922

> Well the Gebru & Mitchell paper[1] was published before In-Context-Learning was discovered (an ICL was very unexpected), so yes, I think they were ignorant of ICL at the time.

??? One of us is confused here (I'm fully willing to admit that it's me), but AFAIK, the big discovery of in-context learning was described in "Language Models are Few-Shot Learners" - published in 2020[1], vs 2021 for the stochastic parrots paper.

Regardless, it's not like the authors of the stochastic parrots paper have disavowed the term. They're still referring to the paper without correction in the statement they published about the proposed pause last month: https://www.dair-institute.org/blog/letter-statement-March20...

[1] https://arxiv.org/pdf/2005.14165.pdf