Are Emergent Abilities of Large Language Models a Mirage? (2304.15004v2)

Abstract: Recent work claims that LLMs display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models. What makes emergent abilities intriguing is two-fold: their sharpness, transitioning seemingly instantaneously from not present to present, and their unpredictability, appearing at seemingly unforeseeable model scales. Here, we present an alternative explanation for emergent abilities: that for a particular task and model family, when analyzing fixed model outputs, emergent abilities appear due to the researcher's choice of metric rather than due to fundamental changes in model behavior with scale. Specifically, nonlinear or discontinuous metrics produce apparent emergent abilities, whereas linear or continuous metrics produce smooth, continuous predictable changes in model performance. We present our alternative explanation in a simple mathematical model, then test it in three complementary ways: we (1) make, test and confirm three predictions on the effect of metric choice using the InstructGPT/GPT-3 family on tasks with claimed emergent abilities; (2) make, test and confirm two predictions about metric choices in a meta-analysis of emergent abilities on BIG-Bench; and (3) show to choose metrics to produce never-before-seen seemingly emergent abilities in multiple vision tasks across diverse deep networks. Via all three analyses, we provide evidence that alleged emergent abilities evaporate with different metrics or with better statistics, and may not be a fundamental property of scaling AI models.

• The paper demonstrates that nonlinear metrics can distort continuous improvements into perceived emergent leaps.
• It uses experiments with GPT-3, BIG-Bench tasks, and vision models to reveal measurement artifacts affecting performance perception.
• The study advocates for continuous, linear metrics to accurately capture gradual advancements in large language models.

Reevaluating Emergent Abilities in LLMs Through the Lens of Metrics

Introduction to the Mirage of Emergent Abilities

Research on LLMs has been centered around the notion of emergent abilities — sudden and unpredictable enhancements in performance on specific tasks as the scale of the models increases. These abilities have been deemed as unpredictable and sharp, fundamentally altering our perceptions of how LLMs advance with scale. However, recent studies propose a different viewpoint that challenges the narrative of intrinsic emergent abilities within LLMs. This paper suggests that what has been interpreted as emergent abilities may, in fact, be an artifact induced by the choice of metrics used by researchers to measure performance.

The Alternative Explanation

The central thesis of this paper is that the perceived emergent abilities in LLMs are not inherent properties of the model's sophistication or scale but are instead a byproduct of the application of nonlinear or discontinuous metrics by researchers. This argument is supported by a detailed mathematical model demonstrating how smooth and predictable improvements in LLM performance can be misconstrued as sudden emergent abilities through specific metric choices. These metrics, when applied, deform the true continuous performance improvements into seemingly unpredictable leaps in abilities, creating a mirage of emergence.

Three main points elucidate this thesis:

1. Metrics Influence Perception: The use of nonlinear or discontinuous metrics can significantly distort the analysis, making gradual improvements appear as sudden emergences.
2. Insufficient Data Resolution: The lack of extensive test datasets can lead to inaccurate estimations of a smaller model's capability, exaggerating the appearance of emergence in larger models.
3. Predictable Model Behavior Under Continuous Metrics: When evaluated under continuous and linear metrics, the improvements in model performance align smoothly and predictably with increases in model scale, thereby nullifying any claims of emergent abilities.

Empirical Validation

To substantiate the alternative explanation, the paper undertakes a series of experiments comprising three distinct approaches:

1. Testing with InstructGPT/GPT-3 Model Family: Changing the evaluation metric from nonlinear (e.g., Accuracy) to linear (e.g., Token Edit Distance) metrics showcased smooth, continuous, and predictable performance enhancements, challenging the claims of emergent abilities.
2. Meta-analysis on BIG-Bench Tasks: A comprehensive review revealed that emergent abilities were predominantly reported under specific metrics (e.g., Multiple Choice Grade and Exact String Match), which are either nonlinear or discontinuous. Altering these to continuous metrics eradicated the supposed emergent phenomena.
3. Inducing Seemingly Emergent Abilities in Vision Tasks: By manipulating the metrics, the researchers were able to artificially create emergent abilities in different architectures across vision tasks, further stressing the influence of metric choice in interpreting model capabilities.

Theoretical and Practical Implications

The implications of this research are profound, offering a pivotal reevaluation of what constitutes emergent abilities in LLMs. Theoretically, it challenges the existing narrative by highlighting the impact of measurement choices on the interpretation of model capabilities. Practically, it advises caution in declaring emergent phenomena without considering the influence of evaluation metrics meticulously. Furthermore, the paper paves the way for more standardized approaches to performance measurement in AI research, advocating for transparency in metric selection and suggesting a shift towards continuous, linear metrics to accurately capture the gradual improvements in LLMs.

Looking Towards the Future

Speculating on future developments, this research invites a wider discourse on the methodologies employed to assess and report the capabilities of AI models. It encourages the exploration of alternative frameworks that can more accurately reflect the incremental nature of advancements in model performance. Moreover, such insights call for collaborative efforts to standardize metrics and methodologies across AI research, ensuring that discoveries are genuinely reflective of advancements rather than artifacts of analysis.

In conclusion, this paper presents a compelling case that purported emergent abilities in LLMs are highly dependent on the metrics employed, challenging the community to reassess the foundational understanding of how LLMs evolve with scale.