Measuring the faithfulness of an answer to provided documents means checking whether the answer 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 stay grounded in the provided context. Faithfulness is typically evaluated by comparing the generated answer’s claims to the source documents and verifying that each statement is either directly supported by, logically inferred from, or unmentioned (but not conflicting with) the source.
Automated metrics for faithfulness often rely on natural language understanding techniques. For example, RAGAS (Retrieval-Augmented Generation Assessment) includes a faithfulness metric that uses a two-step approach: first, it extracts all claims from the generated answer using a language model (LM), then checks if each claim is entailed by the source documents using an entailment detection model or a second LM. Other tools, like BERTScore or BLEURT, compare semantic similarity between the answer and the source text, but they’re less precise for faithfulness since they focus on overall alignment rather than claim-level validation. Some frameworks also use precision (percentage of answer claims supported by sources) and recall (how many source facts are included) as proxies. For instance, if a source states, “The Eiffel Tower was completed in 1889,” an answer claiming it was “built in 1889” would score high in faithfulness, while adding “designed by Gustave Eiffel” (if the source omitted the designer) would lower the score.
While automated metrics are useful, they have limitations. Entailment models might miss nuanced contradictions, and LMs used for claim extraction can introduce errors. Developers often combine multiple metrics and human validation for robustness. For example, a pipeline might use RAGAS to filter low-confidence answers, then apply a rule-based check for specific keywords or numbers from the source. Tools like LlamaIndex’s Evaluation module or TruLens offer customizable workflows for this. Ultimately, the choice depends on the use case: high-stakes applications may require stricter validation, while simpler systems might prioritize speed with basic semantic similarity checks.
