Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions

TL;DR

The paper analyzes how to evaluate large language models through a metrics-centered lens, detailing three metric families—MC, TS, and QA—with precise mathematical formulations and their statistical meanings. It connects evaluation concepts to practical biomedical LLM applications, showing how benchmark datasets and downstream tasks shape metric selection, and discusses pervasive issues such as imperfect gold standards and lack of statistical inference. The authors provide repository guidance and advocate for comprehensive, statistically sound evaluation to enable fair comparisons and robust deployment. This work offers a pragmatic, math-grounded roadmap for researchers to select appropriate metrics and benchmarks across domains. The findings underscore the need for richer evaluation strategies that account for imbalanced data, label ambiguity, and uncertainty quantification in LLM assessments.

Abstract

Natural Language Processing (NLP) is witnessing a remarkable breakthrough driven by the success of Large Language Models (LLMs). LLMs have gained significant attention across academia and industry for their versatile applications in text generation, question answering, and text summarization. As the landscape of NLP evolves with an increasing number of domain-specific LLMs employing diverse techniques and trained on various corpus, evaluating performance of these models becomes paramount. To quantify the performance, it's crucial to have a comprehensive grasp of existing metrics. Among the evaluation, metrics which quantifying the performance of LLMs play a pivotal role. This paper offers a comprehensive exploration of LLM evaluation from a metrics perspective, providing insights into the selection and interpretation of metrics currently in use. Our main goal is to elucidate their mathematical formulations and statistical interpretations. We shed light on the application of these metrics using recent Biomedical LLMs. Additionally, we offer a succinct comparison of these metrics, aiding researchers in selecting appropriate metrics for diverse tasks. The overarching goal is to furnish researchers with a pragmatic guide for effective LLM evaluation and metric selection, thereby advancing the understanding and application of these large language models.