To measure “supporting evidence coverage” — the degree to which every part of an answer is grounded in retrieved documents — you need a systematic approach to trace claims in the answer back to specific document snippets. This involves three main steps: segmenting the answer into verifiable claims, aligning each claim with document content, and quantifying the coverage. For example, if an answer states, “The Apollo 11 mission landed on the moon in 1969,” you would check whether a retrieved document explicitly mentions the year 1969, the mission name, and the moon landing. Tools like semantic similarity models (e.g., SBERT) or exact keyword matching can help automate this alignment. The coverage score is typically the percentage of answer claims that have direct support in the documents.
A practical implementation might involve splitting the answer into individual statements or facts and using retrieval-augmented pipelines to map each statement to document passages. For instance, in a question-answering system about climate change, if the answer includes “CO2 levels have risen by 50% since the industrial era,” the system would search documents for phrases like “CO2 increase,” “industrial revolution,” and numerical data supporting the 50% claim. Ambiguities arise when answers paraphrase document content (e.g., “global temperatures surged” vs. “a sharp increase in Earth’s surface temperature”). Here, embedding-based similarity scores (e.g., cosine similarity between sentence vectors) can identify indirect matches, while thresholds (e.g., 0.8 similarity) determine valid support. Partial matches or unsupported claims reduce the overall coverage score.
Developers can use open-source tools like spaCy for sentence segmentation, Hugging Face’s sentence-transformers for semantic comparisons, and custom scripts to calculate coverage metrics. For example, a Python script might iterate through each answer segment, compute its similarity to all document passages, and flag segments with no matches above a predefined threshold. Logging these results helps audit system reliability — e.g., an 85% coverage score means 15% of the answer lacks explicit backing. This process not only validates answers but also identifies gaps in document retrieval (e.g., missing key sources) or overconfident language models. By iterating on these metrics, developers can improve both retrieval quality and answer grounding.
