Language Model Council: Benchmarking Foundation Models on Highly Subjective Tasks by Consensus

• URL: http://arxiv.org/abs/2406.08598v1
• Date: Wed, 12 Jun 2024 19:05:43 GMT
• Title: Language Model Council: Benchmarking Foundation Models on Highly Subjective Tasks by Consensus
• Authors: Justin Zhao, Flor Miriam Plaza-del-Arco, Amanda Cercas Curry,
• Abstract summary: Leaderboards like Arena rank Large Language Models (LLMs) based on how well their responses align with human preferences. We propose a novel benchmarking framework, the Language Model Council (LMC) The LMC operates through a democratic process to: 1) formulate a test set through equal participation, 2) administer the test among council members, and 3) evaluate responses as a collective jury.
• Score: 3.8436076642278754
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of Large Language Models (LLMs) necessitates robust and challenging benchmarks. Leaderboards like Chatbot Arena rank LLMs based on how well their responses align with human preferences. However, many tasks such as those related to emotional intelligence, creative writing, or persuasiveness, are highly subjective and often lack majoritarian human agreement. Judges may have irreconcilable disagreements about what constitutes a better response. To address the challenge of ranking LLMs on highly subjective tasks, we propose a novel benchmarking framework, the Language Model Council (LMC). The LMC operates through a democratic process to: 1) formulate a test set through equal participation, 2) administer the test among council members, and 3) evaluate responses as a collective jury. We deploy a council of 20 newest LLMs on an open-ended emotional intelligence task: responding to interpersonal dilemmas. Our results show that the LMC produces rankings that are more separable, robust, and less biased than those from any individual LLM judge, and is more consistent with a human-established leaderboard compared to other benchmarks.

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