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
arXiv:2607. 19355v1 Announce Type: new Abstract: LLMs are increasingly used with external knowledge sources like the internet.
By Joshua Ashkinaze, Laura Kurek, Alina Faisal, Tongyuan Miao, Mariam Joseph, Ceren Budak, Eric Gilbert
arXiv:2511. 03217v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information.
By Shaghayegh Kolli, Richard Rosenbaum, Timo Cavelius, Lasse Strothe, Andrii Lata, Jana Diesner
The paper introduces FACTGUARD, a multi-task workflow that generates evidence‑grounded answerable and unanswerable questions to improve long‑context reasoning in large language models. It presents FACTGUARD‑BENCH, a benchmark of 25,220 examples with context lengths up to 128K, focusing on Lack of Evidence and Misleading Evidence scenarios. Experiments on eleven LLMs show a reliability gap, with the best model achieving only 71.62% accuracy; training on FACTGUARD‑BENCH raises accuracy to 81.17% and enhances reasoned rejection of unanswerable queries.
By Qian-Wen Zhang, Biao Liu, Fang Li, Jie Wang, Lingfeng Qiao, Yifei Yu, Di Yin, Xing Sun
The paper introduces Evidence-Aligned Entity Verification (EAEV), a method for detecting entity-level hallucinations in retrieval-augmented generation (RAG). EAEV aligns generated entities with retrieved evidence across three dimensions and uses counterfactual stability analysis to maintain robust alignments when evidence changes. Experiments on multiple RAG benchmarks show that EAEV consistently outperforms existing hallucination detection methods and generalizes well.
By Runsong Jia, Zhen Fang, Mengjia Wu, Jie Lu, Yi Zhang
A new large-scale Arabic fact‑checking dataset called Arafa has been created using an automated pipeline that generates claims from Arabic Wikipedia, mutates them into counterfactuals, and validates them against supporting or refuting evidence. The dataset contains 181,976 claim‑evidence pairs labeled as supported, refuted, or not enough information, and human evaluation shows high inter‑annotator agreement and strong validation accuracy. Fine‑tuned transformer models on Arafa achieve a Macro F1‑score of 77%, demonstrating its usefulness for Arabic fact‑checking tasks.
By Christophe Khalil, Shady Elbassuoni, Rida Assaf
arXiv:2607. 10562v1 Announce Type: new Abstract: Evaluating the multi-hop reasoning capabilities of large language models remains a significant challenge.
By JungMin Yun, JuneHyoung Kwon, YoungBin Kim
R2VC is a modular fact‑checking system that separates retrieval, reasoning, verification, and confidence calibration. It uses hybrid sparse‑plus‑dense Wikipedia retrieval, a fine‑tuned generator for structured verdicts, an NLI cross‑encoder for selecting evidence‑based candidates, and a lightweight calibrator for confidence and abstention. On the FEVER benchmark, R2VC improves accuracy by 13.74% over a baseline and shows that verifier‑based candidate selection and calibration are key contributors to performance.
By Dhruv Dixit, Paritosh Pandey
arXiv:2606. 13262v1 Announce Type: new Abstract: Recent approaches combining Large Language Models (LLMs) with retrieval-augmented reasoning have shown promise for automated fact verification.
By Rongxin Yang, Shenghong He, Siyuan Zhu, Chao Yu
arXiv:2608.28018v1 Announce Type: cross
Abstract: Knowledge-intensive reasoning requires Large Language Models (LLMs) to ground answers in provided evidence. When evidence is insufficient, it is desi...
By Vy Nguyen, Ziqi Xu, Jeffrey Chan, Estrid He, Feng Xia, Renqiang Luo, Erik Cambria, Xiuzhen Zhang
arXiv:2602. 20094v2 Announce Type: replace Abstract: As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground their reasoning in causality rather than spurious correlations.
By Yuzhe Wang, Yaochen Zhu, Jundong Li
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.