Evaluating the Performance of Large Language Models via Debates
This addresses the need for scalable and flexible evaluation methods for LLMs, which is crucial for researchers and developers, though it is incremental as it builds on existing benchmarking ideas.
The authors tackled the problem of evaluating large language models (LLMs) by proposing an automated benchmarking framework based on debates between LLMs, judged by another LLM, which assesses skills like argumentative reasoning and inconsistency recognition. They found that this method produces rankings closely aligned with human-based rankings, eliminating the need for costly human crowdsourcing.
Large Language Models (LLMs) are rapidly evolving and impacting various fields, necessitating the development of effective methods to evaluate and compare their performance. Most current approaches for performance evaluation are either based on fixed, domain-specific questions that lack the flexibility required in many real-world applications, or rely on human input, making them unscalable. To address these issues, we propose an automated benchmarking framework based on debates between LLMs, judged by another LLM. This method assesses not only domain knowledge, but also skills such as argumentative reasoning and inconsistency recognition. We evaluate the performance of various state-of-the-art LLMs using the debate framework and achieve rankings that align closely with popular rankings based on human input, eliminating the need for costly human crowdsourcing.