Constructing a Japanese Claim Decomposition Dataset for Fact-Checking of LLM-Generated Texts

ARAFA: An LLM-Generated Arabic Fact-Checking Dataset

Automatic fact-checking poses a significant challenge in Arabic natural language processing due to the scarcity of datasets and resources. In this manuscript, we introduce Arafa, a new large-scale dataset for fact-checking in Modern Standard Arabic, constructed through an automated framework leveraging large language m...

When Citations Mislead? A Claim-Level Benchmark for Legal Hallucination Detection

Large language models are increasingly used in legal research and drafting, but they can still produce claims that sound convincing without being supported by the cited source. We introduce PARCEL, a benchmark for checking whether a legal claim is supported by the underlying authority. Using recent New York State Court...

Document-Level Claim Extraction and Decontextualization Method Based on Large Language Models

Claim extraction and decontextualization are fundamental steps in automated fact-checking tasks. Claim extraction aims to identify core check-worthy claims from unverified text, while decontextualization seeks to eliminate the contextual dependencies of core claims so that they can be understood independently of the or...

Claim level graph reasoning for training free LLM hallucination mitigation

Large language models (LLMs) often generate fluent yet unsupported or contradicted claims, and such hallucinations become particularly harmful when expressed with high confidence. Existing mitigation methods typically verify responses at the answer level or rely on support-oriented retrieval, making it difficult to dis...

FactOReS: Fact-checking with an Evidence-based Open Resource in Spanish

Adaptive NLI-Driven Claim Verification with Statistical Decision Modeling for Low-Latency Hallucination Reduction in Large Language Models.

A lightweight two-step claim verification framework that decomposes LLM responses into atomic factual claims and independently verifies each extracted claim against a separately generated reference produced through an isolated factual recall prompt, showing consistent performance across the evaluated benchmarks without...