arXiv Computation and Language
Quantifying Claim Smoothing by LLM Research Agents
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
arXiv AI
Sep 1
Redesigning and Auditing Deep Research Writing for Faithful Reports
The paper introduces CLAIMPROBE, a claim-level audit tool that breaks down deep-research reports into individual claims and evaluates them for hallucination, misattribution, citation hygiene, and necessary-fact recall against retrieved evidence. Using CLAIMPROBE, the authors show that even high-scoring deep-research pipelines can omit key evidence and misattribute claims. They also propose CLAIMWRITER, a hierarchical claim-based writer that extracts source facts, maps them to an outline, and drafts sections from a source-linked claim representation, which reduces hallucination by 2.6 to 4.5 times and improves necessary-fact recall by 1.2 to 1.7 times while preserving overall report quality and enabling efficient localized revisions.
arXiv AI
Oct 9
When Citations Mislead? A Claim-Level Benchmark for Legal Hallucination Detection
The paper introduces PARCEL, a benchmark designed to evaluate whether legal claims are supported by cited authorities. Using 3,396 claims from New York State Court of Appeals decisions, the authors label each claim as Supported, Refuted, or Not Found and cast the task as a three‑way natural language inference problem. State‑of‑the‑art LLMs achieve up to 0.97 accuracy in zero‑shot settings, yet they still frequently misclassify unsupported claims as supported, especially when fabricated but plausible citations are present.
arXiv Computation and Language
Oct 9
CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence
CiteVQA is a benchmark that evaluates multimodal large language models on both answering document questions and providing element‑level bounding‑box citations for the evidence used. It contains 1,897 questions across 711 PDFs in seven domains and two languages, with ground‑truth citations generated via an automated pipeline and strict quality control. The evaluation metric, Strict Attributed Accuracy (SAA), rewards a prediction only when both the answer and the cited region are correct, revealing widespread attribution hallucination in current models.
arXiv Computation and Language
Oct 12
OARelatedWork: A Large-Scale Dataset of Related Work Sections with Full-texts from Open Access Sources
OARelatedWork is a large‑scale dataset for generating entire related‑work sections from open‑access papers, comprising 94,450 papers across multiple domains. It includes full texts of cited works and is designed so every source paper is available in full, enabling controlled evaluation of full‑text related‑work generation. The paper benchmarks various models, shows that large language models struggle with massive full‑text contexts, and introduces a statement‑level evaluation framework to better assess long‑form structured outputs.
arXiv Computation and Language
Sep 23
Peerify: Benchmarking Peer-Review Claim Verification
arXiv:2609.25046v1 Announce Type: new Abstract: Peer review plays a central role in scholarly publishing, yet verifying whether reviewer claims are supported by manuscript evidence remains a largely...
Hugging Face Trending Papers
Jun 11
Authority, Truth, and Citation Bias: A Large-Scale Multi-Domain Benchmark for Studying Epistemic Susceptibility in Large Language Models
Large language models are increasingly deployed in citation-augmented settings, yet the effect of citation presence on model behavior independent of factual content remains poorly understood. We introduce AuthorityBench, a 220,564-prompt multi-domain benchmark that isolates how citation-based authority signals influence epistemic behavior in LLMs.