GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering
<|reference_start|>GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering: Retrieval-Augmented Generation (RAG) has emerged as a common paradigm to use Large Language Models (LLMs) alongside private and up-to-date knowledge bases. In this work, we address the challenges of using LLM-as-a-Judge when evaluating grounded answers generated by RAG systems. To assess the calibration and discrimination capabilities of judge models, we identify 7 generator failure modes and introduce GroUSE (Grounded QA Unitary Scoring of Evaluators), a meta-evaluation benchmark of 144 unit tests. This benchmark reveals that existing automated RAG evaluation frameworks often overlook important failure modes, even when using GPT-4 as a judge.   To improve on the current design of automated RAG evaluation frameworks, we propose a novel pipeline and find that while closed models perform well on GroUSE, state-of-the-art open-source judges do not generalize to our proposed criteria, despite strong correlation with GPT-4's judgement. Our findings suggest that correlation with GPT-4 is an incomplete proxy for the practical performance of judge models and should be supplemented with evaluations on unit tests for precise failure mode detection.   We further show that finetuning Llama-3 on GPT-4's reasoning traces significantly boosts its evaluation capabilities, improving upon both correlation with GPT-4's evaluations and calibration on reference situations.<|reference_end|>
@article{muller2024grouse:,
    title={GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question
  Answering},
    author={Sacha Muller, Ant'onio Loison, Bilel Omrani and Gautier Viaud},
    journal={arXiv preprint arXiv:2409.06595},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06595},
    primaryClass={cs.CL}
}