ElementCheck is a new framework for evaluating the factuality of long-form text that addresses limitations of the traditional decompose‑retrieve‑verify pipeline. Rather than breaking sentences into atomic sub‑claims, it extracts entity pairs linked by verifiable connections to form an element graph, using the graph’s topology to gauge sentence complexity. This allows simple sentences to be verified directly while complex ones undergo targeted element‑level refinement, and the authors introduce the FastFact‑Sent benchmark to support fine‑grained evaluation, demonstrating consistent improvements across multiple backbone models.
By Xinming Wang, Haoran Du, Yi Chen, Jian Xu, Hongming Yang, Han Hu, Yulong Chen, Cheng-Lin Liu, Xu-Yao Zhang
Systematically evaluating the factuality of large language models with the FACTS Benchmark Suite.
AEScorer is an agentic evidence‑grounded framework designed for graded factuality verification, addressing the limitation of binary judgments in current methods. It operates in two stages: first, it gathers and refines external evidence through agentic search; second, it predicts a scalar factuality score to capture nuanced differences in correctness. The authors also introduce GradedVeriBench, a benchmark covering general and multi‑hop question answering, and demonstrate that AEScorer outperforms existing approaches on this new benchmark.
By Hui Huang, Muyun Yang, Yuki Arase
The paper introduces Fact-Ablated Evaluation (FAE), a framework that iteratively removes cited evidence to test whether large language models (LLMs) adjust their fact‑checking predictions accordingly. Experiments reveal that many off‑the‑shelf LLMs rely more on internal knowledge than on the provided evidence. To address this, the authors propose REAL, a training method that uses counterfactual evidence supervision to encourage LLMs to base veracity judgments on evidence, achieving better evidence‑dependent performance across four datasets.
By Xingyu Deng, Mingzi Cao, Nikolaos Aletras, Xi Wang, Mark Stevenson
arXiv:2609.00241v1 Announce Type: new
Abstract: Long documents often distribute important information across extensive narrative passages and multiple tables, making faithful summarization particular...
By Meng Zhou, Wenhao You, Wei Yuan
arXiv:2606. 19819v1 Announce Type: cross Abstract: Decomposing compound sentences into atomic, verifiable claims is a prerequisite for reliable automated fact-checking.
By Phuong Huu Vu Tran, Thuan Duc Mai, Bach Xuan Le
Large Language Models (LLMs) generate fluent long-form text, however, often add unsupported factual claims. Existing verification techniques improve factuality by grounding generation in external evidence.
arXiv:2606. 26437v1 Announce Type: cross Abstract: Existing metrics for factuality and faithfulness evaluate whether an answer is supported or contradicted by its grounding documents, but they fail to capture when both supporting and contradicting evidence coexist.
By Siyi Liu, Aaron Halfaker, Dan Roth, Patrick Xia
arXiv:2609.00494v1 Announce Type: new
Abstract: LLM-based factuality judges provide scalable evaluation signals, but their metrics are often systematically biased relative to human judgments. We stud...
By Yu Wang, Craig Erickson, Kevin Small
arXiv:2606. 15246v1 Announce Type: cross Abstract: Provenance-enhanced statements of the form "according to $X$, $\varphi$" are pervasive in contemporary knowledge graphs, especially in domains where graph content primarily represents claims, interpretations, and hypotheses (\emph{capta}) rather than observer-independent facts (\emph{data}).
By Fabio Vitali, Valentina Pasqual
The paper introduces DEDUCE, a three‑stage framework that turns large language models into proactive error correctors by detecting input fact errors, devising correction strategies, and delivering reliable answers. It also presents MisFactQA, a dataset of factual errors, and new metrics for robustness evaluation. Experiments on TruthfulQA, FalseQA, and MisFactQA show significant gains in accuracy and error correction across Qwen, LLaMA, and Gemma models.
By Ping Wang, Xiangguo Sun, Bingbing Xu, Guocong Li, Xiaofeng Meng
arXiv:2606. 05463v1 Announce Type: new Abstract: Patient safety event triage, determining whether a clinical event is reportable under jurisdiction-specific policy, is a high-stakes task typically performed manually by patient safety experts.
By Keqi Han, Ryan Young, Annabel Strauss, Lindsey Hughes, Katharine M. Nesbitt, Nicole Schueler, Che Ngufor, Carl Yang, Yuan Xue, Zhijun Yin