Rethinking Generative Large Language Model Evaluation for Semantic Comprehension

• URL: http://arxiv.org/abs/2403.07872v1
• Date: Tue, 12 Mar 2024 17:59:48 GMT
• Title: Rethinking Generative Large Language Model Evaluation for Semantic Comprehension
• Authors: Fangyun Wei, Xi Chen, Lin Luo
• Abstract summary: This paper revisits the prevalent evaluation method-multiple choice question answering (MCQA), which allows for straightforward accuracy measurement. We introduce an RWQ-Elo rating system, engaging 24 large language models (LLMs) in a two-player competitive format, with GPT-4 serving as the judge. This system is designed to mirror real-world usage, and for this purpose, we have compiled a new benchmark called Real-world questions'' (RWQ) Our analysis reveals the stability of our RWQ-Elo system, the feasibility of registering new models, and its potential to
• Score: 27.21438605541497
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite their sophisticated capabilities, large language models (LLMs) encounter a major hurdle in effective assessment. This paper first revisits the prevalent evaluation method-multiple choice question answering (MCQA), which allows for straightforward accuracy measurement. Through a comprehensive evaluation of 24 models across 11 benchmarks, we highlight several potential drawbacks of MCQA, for instance, the inconsistency between the MCQA evaluation and the generation of open-ended responses in practical scenarios. In response, we introduce an RWQ-Elo rating system, engaging 24 LLMs such as GPT-4, GPT-3.5, Google-Gemini-Pro and LLaMA-1/-2, in a two-player competitive format, with GPT-4 serving as the judge. Each LLM receives an Elo rating thereafter. This system is designed to mirror real-world usage, and for this purpose, we have compiled a new benchmark called ``Real-world questions'' (RWQ), comprising 20,772 authentic user inquiries. Additionally, we thoroughly analyze the characteristics of our system and compare it with prior leaderboards like AlpacaEval and MT-Bench. Our analysis reveals the stability of our RWQ-Elo system, the feasibility of registering new models, and its potential to reshape LLM leaderboards.

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