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arXiv:2611.06090v1 Announce Type: new Abstract: This paper presents an overview of \emph{SciClaimEval}, a pilot task at NTCIR-19 on verifying scientific claims against the tables and figures of the p...
arXiv:2609.00654v1 Announce Type: new Abstract: We describe the SciTrue team's participation in both subtasks of the NTCIR-19 SciClaimEval task~\cite{sciclaimeval}, which asks systems to verify scien...
The study evaluates hybrid retrieval, NLI, and open‑source LLMs for scientific claim verification. Using a benchmark of 2,035 claims, the authors compare a DeBERTa‑based NLI baseline with Llama 3.1, Qwen3, and Mistral LLMs, finding that Qwen3 achieves the highest Macro‑F1 score. A conservative fusion rule that labels a claim as Support or Contradict only when both models agree actually degrades performance, and error analysis reveals that disagreements often overturn correct LLM predictions.
arXiv:2607. 16131v1 Announce Type: cross Abstract: Multimodal Scientific Claim Verification (MSCV) requires models to verify scientific claims using visually grounded evidence from papers, including figures, tables, charts, and textual context.
arXiv:2603. 05308v3 Announce Type: replace-cross Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and claim verification.
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.