VeriFact: Enhancing Long-Form Factuality Evaluation with Refined Fact Extraction and Reference Facts
Journal:
arXiv
Published Date:
May 14, 2025
Abstract
Large language models (LLMs) excel at generating long-form responses, but
evaluating their factuality remains challenging due to complex inter-sentence
dependencies within the generated facts. Prior solutions predominantly follow a
decompose-decontextualize-verify pipeline but often fail to capture essential
context and miss key relational facts. In this paper, we introduce VeriFact, a
factuality evaluation framework designed to enhance fact extraction by
identifying and resolving incomplete and missing facts to support more accurate
verification results. Moreover, we introduce FactRBench , a benchmark that
evaluates both precision and recall in long-form model responses, whereas prior
work primarily focuses on precision. FactRBench provides reference fact sets
from advanced LLMs and human-written answers, enabling recall assessment.
Empirical evaluations show that VeriFact significantly enhances fact
completeness and preserves complex facts with critical relational information,
resulting in more accurate factuality evaluation. Benchmarking various open-
and close-weight LLMs on FactRBench indicate that larger models within same
model family improve precision and recall, but high precision does not always
correlate with high recall, underscoring the importance of comprehensive
factuality assessment.
