Measuring the "faithfulness" of an answer to provided documents involves verifying whether the generated response accurately reflects the information in the source material without introducing unsupported claims or contradictions. This is critical in systems like Retrieval-Augmented Generation (RAG), where answers must align with retrieved context to avoid hallucinations. Faithfulness is typically assessed by comparing the answer’s claims against the source documents, ensuring each statement can be traced back to evidence in the provided context. Manual evaluation involves human reviewers checking for consistency, but this is time-consuming and hard to scale. Automated metrics offer a faster, systematic approach by leveraging natural language processing (NLP) techniques to quantify alignment between the answer and sources.
Automated tools like RAGAS provide specific metrics for faithfulness. RAGAS, for example, uses a two-step process: it first extracts atomic claims from the generated answer, then uses a language model (like GPT-4) to verify if each claim is supported by the provided context. The result is a score reflecting the percentage of claims grounded in the source documents. Other methods include Natural Language Inference (NLI) models, which evaluate whether the answer logically follows from the context (entailment) or contradicts it. For instance, a model like BART or RoBERTa trained on NLI datasets can classify answer sentences as "entailed," "neutral," or "contradicted." Semantic similarity metrics, such as BERTScore, compare embeddings of the answer and context to measure overlap, though this is less precise for factual grounding. Tools like TruLens or FactScore also automate faithfulness checks by combining retrieval accuracy and claim verification.
However, automated metrics have limitations. LLM-based evaluators may inherit biases or errors from the underlying model, and NLI approaches struggle with complex reasoning or indirect support. Semantic metrics can’t distinguish between factual alignment and generic similarity. While tools like RAGAS simplify evaluation, developers should combine automated scores with human spot-checks for high-stakes applications. For practical use, start with a framework like RAGAS for initial testing, validate results with targeted manual reviews, and iterate on prompts or retrieval pipelines to address gaps identified by both methods.