Rethinking Generative Large Language Model Evaluation for Semantic Comprehension

Fangyun Wei, Xi Chen, and Lin Luo question the effectiveness of MCQA (multiple choice question answering) for evaluating LLMs. They introduce the RWQ-Elo rating system where 24 LLMs like GPT-4, GPT-3.5, and LLaMA-1/-2 engage in a two-player format, with GPT-4 as the judge. The ‘Real-world questions’ (RWQ) benchmark comprises over 20,000 authentic user inquiries, providing a practical evaluation scenario. The RWQ-Elo system aims to mirror real-world usage more closely than previous benchmarks like AlpacaEval and MT-Bench. You can delve into the full details of their research and the comparative analysis of the system here.

• Challenges the utility of MCQA for evaluating LLMs
• Introduces RWQ-Elo rating system with a new ‘Real-world questions’ benchmark
• Indicates stability and potential to reshape LLM leaderboards
• Aligns evaluation with practical, real-world scenarios
• Utilizes GPT-4 as a standard for judging other LLMs

By introducing the RWQ-Elo rating system, this work could significantly influence future LLM development and assessment. The emphasis on real-world inquiries ensures that the benchmarks set for these models are not only robust but also relevant to practical applications, possibly driving future AI research and deployments in a more user-centric direction.