Are Emergent Abilities in Large Language Models just In-Context Learning? (2024)

Abstract: Large language models, comprising billions of parameters and pre-trained on extensive web-scale corpora, have been claimed to acquire certain capabilities without having been specifically trained on them. These capabilities, referred to as “emergent abilities,” have been a driving force in discussions regarding the potentials and risks of language models. A key challenge in evaluating emergent abilities is that they are confounded by model competencies that arise through alternative prompting techniques, including in-context learning, which is the ability of models to complete a task based on a few examples. We present a novel theory that explains emergent abilities, taking into account their potential confounding factors, and rigorously substantiate this theory through over 1000 experiments. Our findings suggest that purported emergent abilities are not truly emergent, but result from a combination of in-context learning, model memory, and linguistic knowledge. Our work is a foundational step in explaining language model performance, providing a template for their efficient use and clarifying the paradox of their ability to excel in some instances while faltering in others. Thus, we demonstrate that their capabilities should not be overestimated.

## introduction, motivation and context one of the most captivating aspects of pre-trained language models (plms) is their capacity to acquire a wide range of knowledge across different domains, while being trained primarily through masked language modelling, a task requiring models to predict masked tokens in their input @xcite . the diverse abilities of plms can be categorised into two broad types: formal linguistic abilities and functional linguistic abilities. formal linguistic abilities refer to the understanding of language rules and patterns, which plms, for example, bert @xcite are known to excel at @xcite . the latter category includes a range of abilities akin to human cognition that are necessary for real-world language use and comprehension, such as commonsense knowledge and social awareness. while plms excel at formal linguistic abilities, they have faced challenges in developing functional linguistic abilities @xcite . the introduction of large language models (llms), which are typically generative plms scaled up to billions of parameters and trained on vast, web-scale data corpora, is changing this landscape @xcite @xcite . recent works indicate that llms exhibit emergent abilities, as measured by their above random performance without explicit training on tasks, including those tasks that explicitly require some form of reasoning. an emergent ability was first defined as an ability to solve a task which is absent in smaller models, but present in llms. this definition, introduced approximately concurrently by two works @xcite , is based on the more general definition of emergence in physics: "emergence is when quantitative changes in a system result in qualitative changes in behaviour" @xcite . emergent abilities are implied due to llms' capacity to perform above the random baseline on the corresponding tasks without explicit training on those same tasks. for example, the emergent ability to understand social situations in llms is inferred from llms' performing well above the random baseline on the social iqa @xcite task, which serves to evaluate models' emotional and social intelligence and includes questions such as "carson was excited to wake up to attend school. why did he do this? options: take the big test, go to bed early, just say hello to friend (correct)". ## experimental setup in this section, we present an overview of our experimental methods investigating emergent abilities in the absence of icl. we experiment with 20 models across 22 tasks using two different settings. we use four different evaluation metrics and additionally run multiple tests for bias, including a manual analysis of our results. we present an overview of this setup below, while details on the hyperparameters and training regime are presented in appendix c. ## emergence in gpt in the absence of in-context learning in this and the next section, we highlight a subset of the results with the goal of highlighting the key findings and trends from our experiments. specifically, this section deals with the emergence of functional linguistic abilities in non-instruction-tuned models, and the next section (section 4) focuses on exploring instruction-tuned models and their interplay with icl and emergent abilities. considering that prior research has identified emergent abilities in gptwe prioritise the gpt family in our experimental analysis. figure 1 illustrates the performance of noninstruction-tuned models from the gpt family in the setting where they are prompted without the use of in-context examples (zero-shot). this approach guarantees the exclusion of icl, allowing for a clear assessment of emergent abilities. tasks listed in the first row against a grey background are tasks which have not been found to be emergent by prior work and the rest are those which have been found to be emergent previously. recall that the definition of emergence @xcite requires llms to perform a task above the baseline and do so in a manner that cannot be predicted based on the performance of smaller models. an analysis of figure 1 , presented in ## instruction-tuning as implicit in-context learning the remarkable performance of instruction-tuned models cannot be solely attributed to their pretraining objective, which is to predict the next most probable token. this observation has led to the conjecture that models gain emergent functional linguistic abilities, such as reasoning @xcite . nevertheless, llms exhibit several limitations that are at odds with this view: namely, their known sensitivity to minor prompt variations and their tendency to hallucinate. this leads us to hypothesise that the primary mechanism underlying the capabilities of instruction-tuned models may in fact be an indirect form of icl, which we call 'implicit in-context learning'. this section presents experimental results aimed at discerning whether this is the more plausible explanation underlying the performance of instruction-tuned llms. our evaluation in this section focuses on task solvability rather than performance. this is because the (sometimes wide) variation in parameter counts, architectures, and the pre-training data of the models we compare would necessarily mean that performance may differ across models. however, assessing task solvability offers a clearer insight into emergent abilities within the models. we utilise the previously-introduced bertscore accuracy for all scenarios and evaluate models across the same 22 selected tasks previously outlined in ## related work emergent abilities an emergent ability was first defined as an ability that is not present in smaller models but is present in larger models @xcite . from a review of prior literature of llms including @xcite , chinchilla @xcite , gopher @xcite and lamda @xcite , @xcite identified a total of 67 emergent abilities based on above-random performance of llms on tasks designed to test those abilities from the big-bench dataset @xcite , and the massive multitask language understanding benchmark @xcite . subsequent studies have explored additional abilities emergent in llms, such as theory of mind @xcite and cognitive biases @xcite . however, @xcite have previously questioned the existence of emergent abilities, arguing that emergence is likely to be a consequence of the discrete evaluation metrics commonly employed for assessing llms. some @xcite argue against this by pointing out that there are tasks on which llms are able to perform well above the random baseline where smaller models can only perform below it, suggesting that these abilities are still emergent and not just a consequence of discrete evaluation metrics. similarly, several works @xcite @xcite have explored the extent to which memory plays a role in llms' abilities. in-context learning icl is a learning paradigm that has gained great popularity with the advent of llms @xcite . icl typically involves prompting an llm with in-context demonstrations, and offers a more interpretable interface as well as greater computational efficiency compared to previous learning approaches @xcite . notably, icl has demonstrated strong performance on various natural language tasks @xcite @xcite . in terms of the theoretical rationale for icl in llms, recent work indicates that it might share similarities with fine-tuning, in that it might allow models to "learn" from the examples presented in their prompt @xcite . similarly, it has been shown that icl implements gradient descent implicitly and constructs a function at inference time on regression problems @xcite @xcite , which may be related to gradient-based meta-learning @xcite . a line of work shows that icl is driven by the distributions of the pre-training data @xcite . some other theoretical explorations attempt to explain icl in terms of bayesian inference @xcite @xcite . to the best of our knowledge, none of the previous evaluations of emergent abilities have been conducted in a manner that explicitly distinguished between the icl and instruction-tuning settings and prompting in the setting wherein these abilities are not triggered. ## conclusions and implications we started with two hypotheses: a) that the emergence of all previously-observed functional linguistic abilities is a consequence of icl, and b) that the abilities which present themselves in instructiontuned llms is more likely to be indicative of instruction-tuning resulting in implicit icl, rather than the emergence of functional linguistic abilities. our results confirmed both of these hypotheses. the distinction between the ability to follow instructions and the inherent ability to solve a problem is a subtle but important one, and bears significance to the methods employed in utilising llms and the problems they are tasked with solving. simple following of instructions without applying reasoning abilities produces output that is consistent with the instructions, but might not make sense on a logical or commonsense basis. this is reflected in the well-known phenomenon of 'hallucination', in which an llm produces fluent, but factually incorrect output @xcite the ability to follow instructions does not imply having reasoning abilities, and more importantly, it does not imply the possibility of latent, potentially-dangerous abilities. additionally, these observations imply that our findings hold true for any model which exhibits a propensity for hallucination or requires prompt engineering, including those with greater complexity, regardless of scale or number of modalities, such as gpt-4. by contributing to a deeper understanding of these models' abilities and limitations, we help to demystify llms, alleviate their related safety concerns, and lay out a framework for their more efficient use.