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.
Automated fact-checking (AFC) systems retrieve evidence and predict claim veracity, yet evaluations omit simple baselines, systems are developed for a single benchmark and cannot be trusted to general...
The paper evaluates the robustness of automated fact‑checking systems by cross‑benchmarking nine models—including random baselines, fine‑tuned transformers, zero‑shot LLMs, and top AVeriTeC 2025 systems—across four datasets from scientific, open‑web, and climate domains. It finds that fine‑tuned models outperform zero‑shot LLMs on ClimateCheck, that system rankings vary strongly with domain and metric, and that replacing retrieved evidence with gold annotations boosts veracity accuracy by 14–22 points, underscoring retrieval as the main bottleneck. The authors provide code, pre‑processed datasets, and results to enable reproducible research.
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.
arXiv:2607. 18240v1 Announce Type: new Abstract: Large language models (LLMs) can achieve strong fact-checking accuracy, yet forced binary decisions conceal a critical reliability problem: systems may issue confident verdicts even when supporting evidence is weak, sparse, or internally inconsistent.
MedRAGChecker is a claim-level verification framework designed for biomedical retrieval‑augmented generation (RAG). It decomposes generated answers into atomic claims and assesses each claim’s support by combining evidence‑grounded natural language inference with biomedical knowledge‑graph consistency signals. The aggregated claim decisions provide diagnostics that distinguish retrieval and generation failures, such as faithfulness, under‑evidence, contradiction, and safety‑critical errors, and the system is distilled into compact models for scalable evaluation.
The paper introduces BioCheck Agent, an LLM-based system that generates structured biomedical fact‑checking reports using agentic search and a reinforcement‑learning framework called EG‑GRPO. Unlike prior methods that output only supported or refuted labels, BioCheck Agent synthesizes conclusions with retrieved evidence from PubMed, employing advanced Boolean search operators. Experiments show that, compared to the base Qwen3.5‑4B model, BioCheck Agent improves label prediction accuracy on SciFact by 9.95 %, raises evidence quality by 3.7 %, and reduces hallucinations by 19.63 %.
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.
Fact-checking systems built on LLMs achieve high verdict accuracy on standard benchmarks, yet routinely output Supports labels whose cited evidence does not license the claim. Structured decomposition is the natural way to inspect those warrants, but rigid extraction protocols strip the full-claim context that facets need.
The paper presents the first large‑scale benchmark for uncertainty quantification (UQ) calibration in long‑form scientific question answering, evaluating four UQ methods on 685,000 responses from up to 20 large language models across seven datasets. It shows that instruction tuning leads to token‑level probability polarization, undermining token‑level uncertainty signals, while reasoning model families differ in how they handle this effect. Only semantic consistency—consistency of the final answer—provides well‑calibrated outputs, demonstrating that semantic calibration remains robust in multi‑step, dependency‑rich reasoning.
The paper investigates evidence generation for biomedical claim verification, evaluating various large language models and retrieval strategies on the CARE-XAI benchmark. It finds that fine‑tuned LLMs excel at producing evidence, while biomedical classifiers still lead in verdict‑only prediction. PubMed retrieval helps on PubMed‑aligned datasets but can mislead on broader public‑health claims, prompting the authors to propose Bio‑GRACE, a diagnostic that normalizes gold references to assess retrieval utility.
arXiv:2608. 16643v1 Announce Type: cross Abstract: Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation.
Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation. Error-detection benchmarks are typically constructed by injecting errors into notes, such that each erroneous note has a natural counterpart.