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ACL Findings2023

Evaluation for Change

Rishi Bommasani

摘要

Evaluation is the central means for assessing, understanding, and communicating about NLP models. In this position paper, we argue evaluation should be more than that: it is a force for driving change, carrying a sociological and political character beyond its technical dimensions. As a force, evaluation's power arises from its adoption: under our view, evaluation succeeds when it achieves the desired change in the field. Further, by framing evaluation as a force, we consider how it competes with other forces. Under our analysis, we conjecture that the current trajectory of NLP suggests evaluation's power is waning, in spite of its potential for realizing more pluralistic ambitions in the field. We conclude by discussing the legitimacy of this power, who acquires this power and how it distributes. Ultimately, we hope the research community will more aggressively harness evaluation to drive change.

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它引用的 Paper6

  1. Adversarial NLI: A New Benchmark for Natural Language UnderstandingYixin Nie, Adina Williams, Emily Dinan, Mohit Bansal 等ACL 2020 · 602 次引用
  2. An empirical analysis of compute-optimal large language model trainingJordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya 等NeurIPS 2022 · 566 次引用
  3. Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset DevelopmentMorgan Klaus Scheuerman, Alex Hanna, Emily DentonCSCW 2021 · 169 次引用
  4. FLEX: Unifying Evaluation for Few-Shot NLPJonathan Bragg, Arman Cohan, Kyle Lo, Iz BeltagyNeurIPS 2021 · 114 次引用
  5. How Reliable are Model Diagnostics?Vamsi Aribandi, Yi Tay, Donald MetzlerACL Findings 2021 · 15 次引用

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