🤖 AI Summary
This work addresses the limitations of existing automatic evaluation methods for large language model (LLM) outputs, which often rely on reference texts and exhibit limited generalizability, thereby struggling to accurately assess the quality and relevance of generated content. The authors propose a reference-free, domain-agnostic automated evaluation framework that leverages pairwise comparisons among multiple LLMs, integrated with an Elo rating system to produce stable and interpretable rankings. A tunable consistency threshold is introduced to balance evaluation confidence against coverage. Evaluated on scientific abstract quality assessment, the method yields rankings that align closely with expert judgments, significantly reducing the need for manual evaluation while demonstrating near-expert assessment capability.
📝 Abstract
Evaluating the quality and relevance of textual outputs from Large Language Models (LLMs) remains challenging and resource-intensive. Existing automated metrics often fail to capture the complexity and variability inherent in LLM-generated outputs. Moreover, these metrics typically rely on explicit reference standards, limiting their use mostly to domains with objective benchmarks. This work introduces a novel evaluation framework designed to approximate expert-level assessments of LLM-generated content. The proposed method employs pairwise comparisons of outputs by multiple LLMs, reducing biases from individual models. An Elo rating system is used to generate stable and interpretable rankings. Adjustable agreement thresholds, from full unanimity to majority voting, allow flexible control over evaluation confidence and coverage. The method's effectiveness is demonstrated through evaluating competency profiles extracted from scientific abstracts. Preliminary results show that automatically derived rankings correlate well with expert judgments, significantly reducing the need for extensive human intervention. By offering a scalable, consistent, and domain-agnostic evaluation layer, the framework supports more efficient and reliable quality assessments of LLM outputs across diverse applications.