GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question
  Answering
Authors: name: Sacha Muller, name: António Loison, name: Bilel Omrani, name: Gautier Viaud

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.