Pseudointelligence: A Unifying Framework for Language Model Evaluation

Abstract

With large language models surpassing human performance on an increasing number of benchmarks, we must take a principled approach for targeted evaluation of model capabilities. Inspired by pseudorandomness, we propose pseudointelligence, which captures the maxim that “perceived intelligence lies in the eye of the beholder”. That is, that claims of intelligence are meaningful only when their evaluator is taken into account. Concretely, we propose a complexity-theoretic framework of model evaluation cast as a dynamic interaction between a model and a learned evaluator. We demonstrate that this framework can be used to reason about two case studies in language model evaluation, as well as analyze existing evaluation methods.

Publication
Findings of the Association for Computational Linguistics: Empirical Methods in Natural Language Processing (EMNLP 2023); Selected for the Generation, Evaluation & Metrics (GEM) Workshop at Empirical Methods in Natural Language Processing (GEM @ EMNLP 2023)