FactLens: Benchmarking Fine-Grained Fact Verification
This addresses the issue of hallucination in LLMs for users needing accurate fact-checking, though it is incremental as it builds on existing verification approaches.
The paper tackles the problem of verifying factually incorrect information from LLMs by proposing a shift to fine-grained verification, where complex claims are broken into sub-claims, and introduces FactLens, a benchmark with metrics and automated evaluators that show alignment with human judgments.
Large Language Models (LLMs) have shown impressive capability in language generation and understanding, but their tendency to hallucinate and produce factually incorrect information remains a key limitation. To verify LLM-generated contents and claims from other sources, traditional verification approaches often rely on holistic models that assign a single factuality label to complex claims, potentially obscuring nuanced errors. In this paper, we advocate for a shift towards fine-grained verification, where complex claims are broken down into smaller sub-claims for individual verification, allowing for more precise identification of inaccuracies, improved transparency, and reduced ambiguity in evidence retrieval. However, generating sub-claims poses challenges, such as maintaining context and ensuring semantic equivalence with respect to the original claim. We introduce FactLens, a benchmark for evaluating fine-grained fact verification, with metrics and automated evaluators of sub-claim quality. The benchmark data is manually curated to ensure high-quality ground truth. Our results show alignment between automated FactLens evaluators and human judgments, and we discuss the impact of sub-claim characteristics on the overall verification performance.