A Sanity Check on ‘Emergent Properties’ in Large Language Models | by Anna Rogers

What this exchange shows is the idea of LLM ‘emergent properties’ per definition (1) has implications outside the research world. It contributes to the x.com/BasedNorthmathr/status/1797142896069488857″ rel=”noopener ugc nofollow” target=”_blank”>anxiety about the imminent takeover by super-AGI, to ai-experiments/” rel=”noopener ugc nofollow” target=”_blank”>calls for pausing research. It could push the policy-makers in the wrong directions, such as banning open-source research — which would further consolidate resources in the hands of a few big tech labs, and ensure they won’t have much competition. It also creates the impression of LLMs as entities independent on the choices of their developers and deployers — which has huge implications for who is accountable for any harms coming from these models. With such high stakes for the research community and society, shouldn’t we at least make sure that the science is sound?

Much in the above versions of ‘emergence’ in LLMs is still debatable: how much do they actually advance the scientific discussion, with respect to other terms and known principles that are already in use? I would like to stress that this discussion is completely orthogonal to the question of whether LLMs are useful or valuable. Countless models have been and will be practically useful without claims of emergence.

Let us start with definition 2: something that a model learned from the training data. Since this is exactly what a machine learning model is supposed to do, does this version of ‘emergence’ add much to ‘learning’?

For the definition (3) (something that only large models do), the better performance of larger models is to be expected, given basic machine learning principles: the larger model simply has more capacity to learn the patterns in its training data. Hence, this version of ‘emergence’ also does not add much. Unless we expect that the larger models, but not the small ones, do something they weren’t trained for — but then this definition depends on definition (1).

For the definition (4), the phenomenon of sharp change in performance turned out to be attributable to non-continuous evaluation metrics (e.g. for classification tasks like multi-choice question answering), rather than LLMs themselves (Schaeffer, Miranda, & Koyejo, 2023). Furthermore, J. Wei himself acknowledges that the current claims of sharp changes are based on results from models that are only available in relatively few sizes (1B, 7B, 13B, 70B, 150B…), and if we had more results for intermediate model sizes, the increase in performance would likely turn out to be smooth (Wei, 2023).

The unpredictability part of definition (4) was reiterated by J. Wei (2023) as follows: “the “emergence” phenomenon is still interesting if there are large differences in predictability: for some problems, performance of large models can easily be extrapolated from performance of models 1000x less in size, whereas for others, even it cannot be extrapolated even from 2x less size.”

However, the cited predictability at 1,000x less compute refers to the GPT-4 report (OpenAI, 2023), where the developers knew the target evaluation in advance, and specifically optimized for it. Given that, predictable scaling is hardly surprising theoretically (though still impressive from the engineering point of view). This is in contrast with the unpredictability at 2x less compute for unplanned BIG-Bench evaluation in (Wei et al., 2022). This unpredictability is expected, simply due to the unknown interaction between (a) the presence of training data that is similar to test data, and (b) sufficient model capacity to learn some specific patterns.

Hence, we are left with the definition (1): emergent properties are properties that the model was not explicitly trained for. This can be interpreted in two ways:

5. A property is emergent if the model was not exposed to training data for that property.

6. A property is emergent even if the model was exposed to the relevant training data — as long as the model developers were unaware of it.

Per def. 6, it would appear that the research question is actually ‘what data exists on the Web?’ (or in proprietary training datasets of generative ai companies), and we are training LLMs as a very expensive method to answer that question. For example, ChatGPT can generate chess moves that are plausible-looking (but often illegal). This is surprising if we think of ChatGPT as a language model, but not if we know that it is a model trained on a web corpus, because such a corpus would likely include not only texts in a natural language, but also materials like chess transcripts, ascii art, midi music, programming code etc. The term ‘language model’ is actually a misnomer — they are rather corpus models (Veres, 2022).

Per def. 5, we can prove that some property is emergent only by showing that the model was not exposed to evidence that could have been the basis for the model outputs in the training data. And it cannot be due to lucky sampling in the latent space of the continuous representations. If we are allowed to generate as many samples as we want and cherry-pick, we are eventually going to get some fluent text even from a randomly initialized model — but this should arguably not count as an ‘emergent property’ on definition (5).

For commercial models with undisclosed training data such as ChatGPT, such a proof is out of the question. But even for the “open” LLMs this is only a hypothesis (if not wishful thinking), because so far we are lacking detailed studies (or even a methodology) to consider the exact relation between the amount and kinds of evidence in the training text data for a particular model output. On definition 5, emergent properties are a machine learning equivalent of alchemy — and the bar for postulating that should be quite high.

Especially in the face of evidence to the contrary.

Here are some of the empirical results that make it dubious that LLMs have ‘emergent properties’ by definition (5) (the model was not exposed to training data for that property):

• Phenomenon of prompt sensitivity (Lu, Bartolo, Moore, Riedel, & Stenetorp, 2022; Zhao, Wallace, Feng, Klein, & Singh, 2021): LLMs responding differently to prompts that should be semantically equivalent. If we say that models have an emergent property of answering questions, slightly different ways of posing these questions, and especially different order of few-shot examples, should not matter. The most likely explanation for the prompt sensitivity is that the model responds better to prompts that are more similar to its training data in some way that helps the model.
• Liang et. al evaluate 30 LLMs and conclude that “regurgitation (of copyrighted materials) risk clearly correlates with model accuracy’’ (2022, p. 12). This suggests that models which ‘remember’ more of training data perform better.
• McCoy, Yao, Friedman, Hardy, & Griffiths (2023) show that LLM performance depends on probabilities of output word sequences in web texts.
• Lu, Bigoulaeva, Sachdeva, Madabushi, & Gurevych (2024) show that the ‘emergent’ abilities of 18 LLMs can be ascribed mostly to in-context learning. Instruction tuning facilitates in-context learning, but does not seem to have an independent effect.
• For in-context learning itself (first shown in GPT-3 (Brown et al., 2020), and used as the example of ‘emergence’ by Bommasani et al. (2021, p. 5), the results of Chen, Santoro et al. (2022) suggest that it happens only in Transformers trained on sequences, structurally similar to the sequences in which in-context learning would be tested.
• Liu et al. (2023) report that ChatGPT and GPT-4 perform better on older compared to newly released benchmarks, suggesting that many evaluation results may be inflated due to data contamination. OpenAI itself went to great lengths in the GPT-3 paper (Brown et al., 2020) showing how difficult it is to mitigate this problem. Since we know nothing about the training data of the latest models, external evaluation results may not be meaningful, and internal reports by companies that sell their models as a commercial service have a clear conflict of interest.

A well-known effort to propose a methodology that would avoid at least the data contamination problem is the ‘sparks of AGI’ study (Bubeck et al., 2023). Using the methodology of newly constructed test cases, checked against public web data, and their perturbations, the authors notably concluded that GPT-4 possesses “a very advanced theory of mind’’. At least two studies have come to the opposite conclusion (Sap, Le Bras, Fried, & Choi, 2022; Shapira et al., 2024). The most likely reason for the failure of this methodology is that while we can check for direct matches on the web, we could still miss some highly similar cases (e.g. the well-known example of unicorn drawn in tikz from that paper twitter.com/DimitrisPapail/status/1644809234431848450?s=20″ rel=”noopener ugc nofollow” target=”_blank”>could be based on the stackoverflow community drawing other animals in tikz). Furthermore, the commercial LLMs such as GPT-4 could also be trained on data that is not publicly available. In the case of OpenAI, hundreds of researchers and other users of GPT-3 have submitted a lot of data though the API, before OpenAI changed their terms of service to not use such data for training by default.

This is not to say that it is absolutely impossible that LLMs could work well out of their training distribution. Some degree of generalization is happening, and the best-case scenario is that it is due to interpolation of patterns that were observed in training data individually, but not together. But at what point we would say that the result is something qualitatively new, what kind of similarity to training data matters, and how we could identify it — these are all still-unresolved research questions.

As I mentioned, I had a chance to give a talk about this in several NLP research groups. In the very beginning of these talks, before I presented the above discussion, I asked the audience a few questions, including whether they personally believed that LLMs had emergent properties (according to their preferred definition, which, as shown above, was predominantly (1)). I also asked them about their perception of the consensus in the field — what did they think that most other NLP researchers thought about this? For the first question I have answers from 259 researchers and PhD students, and for the second — from 360 (note to self: give people more time to connect to the poll).

The results were striking: while most respondents were skeptical or unsure about LLM emergent properties themselves (only 39% agreed with that statement), 70% thought that most other researchers did believe this.

This is in line with several other false sociological beliefs: e.g. many NLP researchers don’t think that NLP leaderboards are particularly meaningful, or that scaling will solve everything, but they do think that other NLP researchers believe that (Michael et al., 2023). In my sample, the idea that LLM have emergent properties is similarly held by a minority of researchers, but it is misperceived to be the majority. And even for that minority the conviction is not very firm. In four of my talks, after presenting the above discussion, I also asked the audience what they thought now. In this sample of 70 responses, 83% of those who originally agreed with the statement “LLMs have emergent properties”, changed their belief to either disagreeing (13.9%) or being unsure (69.4%).

In retrospect, “agree/disagree/unsure” is not the best choice of options for this poll. As scientists, we can hardly ever be 100% sure: as Yann LeCun put it in the Munk debate, we cannot even prove that there is no teapot orbiting Jupiter right now. Our job is not to fall into such distracting rabbit holes, but to formulate and test hypotheses that would advance our understanding of the phenomenon we are studying. For ‘emergence’ in LLMs, I think we are still at the ‘formulation’ stage — since even after all the above work with clarifying ‘emergence’ we still don’t have a research question, for which it is clear how to obtain empirical evidence.

The key unresolved question is what kind of interpolation of existing patterns would even count as something new enough to qualify as an ‘emergent phenomenon’ in the domain of natural language data. This domain is particularly hard, because it mixes different kinds of information (linguistic, social, factual, commonsense), and that information may be present differently (explicit in context, implicit, or requiring reasoning over long contexts). See Rogers, Gardner, & Augenstein (2023, pp. sec. 8.2) for a discussion of different skills involved in just the question answering task.

If the relationship between LLM output and its training data is a problem that you (or someone you know) would like to figure out — there are funded postdoc / PhD positions to work on it in beautiful Copenhagen! (apply by Nov 15/22 2024)