BLEU, ROUGE, and METEOR are traditional metrics applicable to evaluating RAG-generated answers, though they focus on different aspects of text quality.
BLEU measures precision by comparing n-gram overlaps between the generated answer and reference texts. It focuses on exact word or phrase matches, emphasizing how closely the output aligns with the expected answer. For example, if a RAG system generates "Paris is France's capital," and the reference is "The capital of France is Paris," BLEU would penalize the reordered words despite their correctness. While useful for assessing surface-level accuracy, BLEU struggles with paraphrased or semantically equivalent answers, as it prioritizes lexical similarity over meaning.
ROUGE evaluates recall, measuring how much of the reference content is captured in the generated text. ROUGE-L, for instance, looks at the longest common subsequence to assess overlap in key ideas. For RAG answers, this metric highlights whether critical information from retrieved documents (e.g., facts in a reference article) is included. If a RAG answer omits a key detail like "France's population is 67 million," ROUGE would detect the missing data. However, like BLEU, it relies on exact matches and may undervalue answers that rephrase information effectively.
METEOR balances precision and recall while incorporating synonym matching and stemming. It aligns generated and reference texts using WordNet synonyms, allowing for variations in wording. For example, if a RAG answer uses "automobile" instead of "car," METEOR would recognize the equivalence, unlike BLEU or ROUGE. This makes METEOR better suited for evaluating fluency and paraphrasing quality in RAG outputs. However, it still depends on reference texts and does not directly assess factual correctness if references are incomplete or ambiguous.
Limitations and Context: While these metrics provide insights into lexical overlap and content coverage, they do not evaluate factual accuracy, coherence, or relevance to user intent—critical aspects of RAG systems. They are best used alongside task-specific metrics (e.g., retrieval accuracy scores) or human evaluation for a comprehensive assessment.