The evaluation of Large Language Models (LLMs) increasingly relies on other LLMs acting as judges. However, current evaluation paradigms typically yield a single score or ranking, answering which model is better but not why. While essential for benchmarking, these top-level scores obscure the specific, actionable reasons behind a model's performance. To bridge this gap, we introduce CLEAR, an interactive, open-source package for LLM-based error analysis. CLEAR first generates per-instance textual feedback, then it creates a set of system-level error issues, and quantifies the prevalence of each identified issue. Our package also provides users with an interactive dashboard that allows for a comprehensive error analysis through aggregate visualizations, applies interactive filters to isolate specific issues or score ranges, and drills down to the individual instances that exemplify a particular behavioral pattern. We demonstrate CLEAR analysis for RAG and Math benchmarks, and showcase its utility through a user case study.
@article{arxiv.2507.18392,
title = {CLEAR: Error Analysis via LLM-as-a-Judge Made Easy},
author = {Asaf Yehudai and Lilach Eden and Yotam Perlitz and Roy Bar-Haim and Michal Shmueli-Scheuer},
journal= {arXiv preprint arXiv:2507.18392},
year = {2025}
}