Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
Abstract
Recent work claims that large language models 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 following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper critically examines the concept of emergent abilities in large language models, providing a novel perspective that emphasizes the role of metric choice in evaluating model performance. This work is significant as it encourages a reevaluation of how emergent abilities are perceived and measured, potentially influencing future research directions in the field of machine learning.
Methodology
The paper presents a mathematical model to explain emergent abilities in large language models, focusing on the impact of metric choice on perceived model performance. The methodology is sound, employing a systematic approach to validate predictions through empirical testing across different tasks and model families. The use of both theoretical and empirical analyses strengthens the argument.
Results & evaluation
The experiments are well-designed, utilizing the InstructGPT/GPT-3 family and a meta-analysis of the BIG-Bench dataset to confirm predictions about metric choice. The results are compelling, showing that the choice of metrics can significantly alter the perceived emergence of abilities, which is a critical insight for the field.
Reproducibility
While the paper outlines the methodology and results, it lacks specific implementation details or code availability, which could hinder reproducibility. Providing access to datasets and code would enhance the paper's impact.
Limitations
The primary limitation is the lack of a clear primary institution and the absence of practical implementations or demos. Additionally, the findings may not generalize across all model families or tasks, as the focus is on specific metrics and tasks.
Broader impact
The implications of this work are significant, as it challenges the notion of emergent abilities in large language models and encourages researchers to critically evaluate their metric choices. This could lead to more robust evaluations of model performance and a deeper understanding of model capabilities. The paper critically examines the concept of emergent abilities in large language models, providing a novel perspective that emphasizes the role of metric choice in evaluating model performance. This work is significant as it encourages a reevaluation of how emergent abilities are perceived and measured, potentially influencing future research directions in the field of machine learning.
Curator note
Zheng et al.; LMSYS; Elo-based human preference leaderboard
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