# FactLens: Benchmarking Fine-Grained Fact Verification

arXiv: https://arxiv.org/abs/2411.05980 · HTML: https://mlsanity.com/paper/2411.05980/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019)
- [MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://mlsanity.com/paper/2404.10774/) (2024)
- [LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks](https://mlsanity.com/paper/2406.18403/) (2024)
- [FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information](https://mlsanity.com/paper/2106.05707/) (2021)
- [Large Language Models are Inconsistent and Biased Evaluators](https://mlsanity.com/paper/2405.01724/) (2024)
- [Fact-Checking Complex Claims with Program-Guided Reasoning](https://mlsanity.com/paper/2305.12744/) (2023)
- [AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web](https://mlsanity.com/paper/2305.13117/) (2023)
- [Fine-grained Fact Verification with Kernel Graph Attention Network](https://mlsanity.com/paper/1910.09796/) (2019)
- [Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers](https://mlsanity.com/paper/2311.09000/) (2023)
- [FactKG: Fact Verification via Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2305.06590/) (2023)
- [The Automated Verification of Textual Claims (AVeriTeC) Shared Task](https://mlsanity.com/paper/2410.23850/) (2024)
- [FLEEK: Factual Error Detection and Correction with Evidence Retrieved from External Knowledge](https://mlsanity.com/paper/2310.17119/) (2023)
- [CoverBench: A Challenging Benchmark for Complex Claim Verification](https://mlsanity.com/paper/2408.03325/) (2024)

## Built on by (most cited first, 10 of 10)

- [Hallucination Detection in LLMs Using Spectral Features of Attention Maps](https://mlsanity.com/paper/2502.17598/) (2025)
- [JADES: A Universal Framework for Jailbreak Assessment via Decompositional Scoring](https://mlsanity.com/paper/2508.20848/) (2025)
- [Legal LLM Hallucination Should Be Evaluated as Failure of Legal Warrant](https://mlsanity.com/paper/2609.17546/) (2026)
- [FineDialFact: A benchmark for Fine-grained Dialogue Fact Verification](https://mlsanity.com/paper/2508.05782/) (2025)
- [Can LLMs extract human-like fine-grained evidence for evidence-based fact-checking?](https://mlsanity.com/paper/2511.21401/) (2025)
- [AEScorer: An Agentic Evidence-Grounded Framework for Graded Factuality Verification](https://mlsanity.com/paper/2601.03605/) (2026)
- [FactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Grounding](https://mlsanity.com/paper/2602.03417/) (2026)
- [CAAFC: Chronological Actionable Automated Fact-Checker for misinformation / non-factual hallucination detection and correction](https://mlsanity.com/paper/2605.12436/) (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) 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.