Adaptively evaluating models with task elicitation

• URL: http://arxiv.org/abs/2503.01986v1
• Date: Mon, 03 Mar 2025 19:04:10 GMT
• Title: Adaptively evaluating models with task elicitation
• Authors: Davis Brown, Prithvi Balehannina, Helen Jin, Shreya Havaldar, Hamed Hassani, Eric Wong,
• Abstract summary: We introduce and validate a framework for evaluating language models called Adaptive Evaluations.<n>We find that frontier models lack consistency when adaptively probed with our framework on a diverse suite of datasets and tasks.<n> Generated questions pass human validity checks and often transfer to other models with different capability profiles.
• Score: 29.704450391533864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Manual curation of evaluation datasets is struggling to keep up with the rapidly expanding capabilities and deployment scenarios of language models. Towards scalable model profiling, we introduce and validate a framework for evaluating LLMs, called Adaptive Evaluations. Adaptive evaluations use scaffolded language models (evaluator agents) to search through a target model's behavior on a domain dataset and create difficult questions (tasks) that can discover and probe the model's failure modes. We find that frontier models lack consistency when adaptively probed with our framework on a diverse suite of datasets and tasks, including but not limited to legal reasoning, forecasting, and online harassment. Generated questions pass human validity checks and often transfer to other models with different capability profiles, demonstrating that adaptive evaluations can also be used to create difficult domain-specific datasets.

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