What are the limitations of using ROUGE or METEOR for RAG evaluation, especially considering there may be multiple correct ways to answer a question with the retrieved info?

ROUGE and METEOR have significant limitations when used to evaluate Retrieval-Augmented Generation (RAG) systems, particularly in scenarios where multiple correct answers are possible based on retrieved information. Both metrics rely on comparing generated text to predefined reference answers, which fails to account for the diversity of valid responses that can arise from different interpretations or presentations of retrieved data. For example, a RAG system answering “What are the health benefits of exercise?” might produce distinct but equally correct answers by emphasizing cardiovascular improvement, mental health benefits, or immune system support. ROUGE and METEOR would penalize these variations unless they closely match the specific wording of the reference answer, even though all responses are factually valid.

A core issue is that these metrics prioritize lexical overlap over semantic correctness. ROUGE measures n-gram matches between generated and reference text, while METEOR adds synonym matching and syntactic analysis. However, neither directly evaluates whether the answer accurately reflects the retrieved content or addresses the query effectively. For instance, if a reference answer states, “Exercise reduces cardiovascular disease risk by improving circulation,” a RAG output saying, “Physical activity enhances blood flow, lowering heart disease likelihood” might receive low ROUGE scores due to limited word overlap, despite being equally accurate. METEOR might slightly improve scoring through synonym recognition (e.g., “exercise” vs. “physical activity”), but it still struggles with paraphrased or reordered explanations of the same concepts.

Additionally, ROUGE and METEOR cannot assess the faithfulness of answers to the retrieved source material. A RAG system might generate a factually incorrect statement that coincidentally matches reference wording, achieving high scores despite being unfaithful to the sources. Conversely, a correct answer derived from retrieved documents but phrased differently could be scored poorly. For example, if a reference answer cites “a 2021 study linking caffeine to reduced Alzheimer’s risk,” but the RAG system correctly cites “a 2020 paper showing coffee consumption delays dementia onset,” the metrics would miss the semantic alignment due to differences in dates and terminology. This makes them unreliable for evaluating whether RAG systems properly ground responses in the provided evidence, a critical requirement for trustworthy outputs.

Developers should supplement these metrics with task-specific evaluations. For example, using human judgments to assess answer quality, implementing checks for factual consistency with source documents, or designing tests that measure coverage of key information points from retrieved content. While ROUGE and METEOR provide quick automated feedback, their inability to handle answer diversity and semantic fidelity limits their utility for robust RAG evaluation.