# VeriFact: Enhancing Long-Form Factuality Evaluation with Refined Fact Extraction and Reference Facts

arXiv: https://arxiv.org/abs/2505.09701 · HTML: https://mlsanity.com/paper/2505.09701/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 7 of 7)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Molecular Facts: Desiderata for Decontextualization in LLM Fact Verification](https://mlsanity.com/paper/2406.20079/) (2024)
- [ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://mlsanity.com/paper/2304.14827/) (2023)
- [Decomposition Dilemmas: Does Claim Decomposition Boost or Burden Fact-Checking Performance?](https://mlsanity.com/paper/2411.02400/) (2024)

## Built on by (most cited first, 11 of 11)

- [DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality](https://mlsanity.com/paper/2603.05912/) (2026)
- [Beyond Precision: Importance-Aware Recall for Factuality Evaluation in Long-Form LLM Generation](https://mlsanity.com/paper/2604.03141/) (2026)
- [Comprehensiveness Metrics for Automatic Evaluation of Factual Recall in Text Generation](https://mlsanity.com/paper/2510.07926/) (2025)
- [Distill and Align Decomposition for Enhanced Claim Verification](https://mlsanity.com/paper/2602.21857/) (2026)
- [Two-Level Meta-Rubrics for Evaluating Open-Ended Generation: GAMUT, a Benchmark for Factual Completeness](https://mlsanity.com/paper/2607.19322/) (2026)
- [Agentic clinical reasoning over longitudinal myeloma records: a retrospective evaluation against expert consensus](https://mlsanity.com/paper/2604.24473/) (2026)
- [The Deliberative Illusion: Diagnosing Factual Attrition and Stance Homogenization in Multi-Agent LLM Deliberation](https://mlsanity.com/paper/2606.03032/) (2026)
- [HiFACTMix: A Code-Mixed Benchmark and Graph-Aware Model for EvidenceBased Political Claim Verification in Hinglish](https://mlsanity.com/paper/2508.10001/) (2025)
- [Does Finetuning with Scientific Data Increase Hallucinations? A Multi-domain Factuality Evaluation of LLMs](https://mlsanity.com/paper/2606.21359/) (2026)
- [TriQua: Reconciling Granularity and Context in Factuality Evaluation](https://mlsanity.com/paper/2608.05228/) (2026)
- [ElementCheck: Complexity-Aware Long-Form Text Factuality Evaluation via Sentence Elements](https://mlsanity.com/paper/2608.26118/) (2026)

## Abstract (arXiv)

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.