To compare a RAG system’s answers to reference answers, common natural language generation metrics include BLEU, ROUGE, and METEOR. These metrics measure overlap between generated and reference text using different approaches. BLEU focuses on n-gram precision (exact word/phrase matches), ROUGE emphasizes recall (coverage of reference content), and METEOR incorporates synonyms and stemming for alignment while balancing precision and recall. While widely used, these metrics were designed for tasks like machine translation or summarization and have notable limitations when applied to RAG systems.
In the context of RAG, these metrics offer a quantitative way to assess surface-level similarity. For example, BLEU might check if technical terms or key phrases in a reference answer (e.g., “climate change accelerates ice melt”) appear verbatim in the RAG output. ROUGE could measure whether all key points from a reference are included, such as a list of causes for an event. METEOR might recognize paraphrases (e.g., “AI models” vs. “neural networks”) through synonym matching. However, RAG outputs often require evaluating factual accuracy, relevance to a query, and logical coherence—areas these metrics don’t directly address. For instance, a RAG answer might rephrase a reference correctly but introduce subtle factual errors (e.g., misstating dates or quantities), which BLEU/ROUGE would overlook if the n-grams match.
The primary limitations stem from their reliance on lexical overlap rather than semantic or factual correctness. First, they fail to detect factual inconsistencies: a RAG answer could include correct n-grams but contradict the reference’s meaning (e.g., “The treaty was signed in 1990” vs. “The treaty was revoked in 1990”). Second, they assume a single “correct” reference answer, but RAG systems often generate valid responses that differ in structure or emphasis. Third, they ignore contextual relevance—a RAG answer might match a reference lexically but fail to address the user’s specific query. For example, if a user asks for “causes of inflation,” a RAG response listing correct but generic economic factors might score highly on ROUGE yet miss query-specific causes like recent policy changes. These metrics also lack robustness to stylistic variations, penalizing valid paraphrases or concise answers that omit redundant reference phrases. While useful for rough comparisons, they should be supplemented with human evaluation or task-specific metrics (e.g., factual accuracy checks) for reliable RAG assessment.