xFinder: Large Language Models as Automated Evaluators for Reliable Evaluation

TL;DR

This paper introduces xFinder, a novel LLM-based evaluator that improves answer extraction accuracy and evaluation reliability by focusing on enhancing the key answer extraction module, outperforming existing methods.

Contribution

The paper proposes xFinder, a new LLM-based evaluation framework with a specialized dataset, significantly improving answer extraction and judgment accuracy over traditional RegEx and judge models.

Findings

xFinder achieves 93.42% extraction accuracy with only 500M parameters.

xFinder's judgment accuracy reaches 97.61%, surpassing existing frameworks.

Improving answer extraction enhances overall evaluation reliability.

Abstract

The continuous advancement of large language models (LLMs) has brought increasing attention to the critical issue of developing fair and reliable methods for evaluating their performance. Particularly, the emergence of cheating phenomena, such as test set leakage and prompt format overfitting, poses significant challenges to the reliable evaluation of LLMs. As evaluation frameworks commonly use Regular Expression (RegEx) for answer extraction, models may adjust their responses to fit formats easily handled by RegEx. Nevertheless, the key answer extraction module based on RegEx frequently suffers from extraction errors. Furthermore, recent studies proposing fine-tuned LLMs as judge models for automated evaluation face challenges in terms of generalization ability and fairness. This paper comprehensively analyzes the entire LLM evaluation chain and demonstrates that optimizing the key answer extraction module improves extraction accuracy and enhances evaluation reliability. Our findings suggest that improving the key answer extraction module can lead to higher judgment accuracy and improved evaluation efficiency compared to the judge models. To address these issues, we propose xFinder, a novel evaluator for answer extraction and matching in LLM evaluation. As part of this process, we create a specialized dataset, the \textbf{K}ey \textbf{A}nswer \textbf{F}inder (KAF) dataset, to ensure effective model training and evaluation. Generalization tests and real-world evaluations show that the smallest xFinder model, with only 500 million parameters, achieves an average extraction accuracy of 93.42\%. In contrast, RegEx accuracy in the best evaluation framework is 74.38\%. The final judgment accuracy of xFinder reaches 97.61\%, outperforming existing evaluation frameworks and judge models.