To design a metric that penalizes ungrounded content in an answer, we can create a precision-like measure that evaluates how much of the answer is supported by provided source documents. The core idea is to break the answer into verifiable units (e.g., claims, facts, or statements) and compare each unit against the documents to determine if it’s substantiated. For example, if an answer states, “The company’s revenue grew 10% in Q2,” the metric would check whether this claim exists in the source material. The final score would represent the proportion of supported content, penalizing unsupported assertions.
A practical implementation could involve two main steps. First, segment the answer into smaller units using natural language processing (NLP) techniques like sentence splitting or clause detection. For each unit, use semantic similarity models (e.g., sentence transformers like BERT) to compare it against document passages. If the similarity score exceeds a predefined threshold, the claim is considered grounded. Alternatively, a trained classifier could predict whether a claim is supported, using labeled data where human annotators mark claims as grounded or ungrounded. The metric’s score would then be calculated as (supported claims) / (total claims). For instance, if an answer contains 10 claims and 7 are supported, the score would be 0.7. This approach mirrors traditional precision metrics but focuses on factual grounding rather than general relevance.
Challenges include handling paraphrasing, partial matches, and implicit reasoning. For example, an answer might rephrase a document’s statement (“Revenue increased by 10%” vs. “Q2 revenue grew 10%”) or infer conclusions not explicitly stated. To address this, the similarity threshold must balance strictness and flexibility, potentially using contextual embeddings instead of keyword matching. Additionally, the metric could incorporate confidence scores—assigning partial credit for weakly supported claims—rather than a binary yes/no. Developers could also weight penalties based on the severity of ungrounded content (e.g., minor inaccuracies vs. entirely fabricated claims). Testing with human-reviewed benchmarks would help calibrate thresholds and validate results. By iterating on these components, the metric can provide a robust measure of answer grounding while remaining adaptable to different domains.
