3 papers
cs.CL2026
GANDR: Claim Auditing for Verifiable Legal Answer Generation
Chen Qian, Yimeng Wang, Yu Chen +2
In high-stakes domains such as legal practice, a language-model answer is only useful to the extent that a reader can verify each claim against the source the system cites. Current…
cs.CL2026
AtomCite: Verification and Correction of Supplied Page-Level Citations in Multi-Page Documents
Chen Qian, Yimeng Wang, Yu Chen +2
Large language models answering questions over multi-page documents are expected to cite the supporting pages, yet supplied citations are sometimes inaccurate, and current evaluati…
cs.AI2026
From "Thinking" to "Justifying": Aligning High-Stakes Explainability with Professional Communication Standards
Chen Qian, Yimeng Wang, Yu Chen +2
Explainable AI (XAI) in high-stakes domains should help stakeholders trust and verify system outputs. Yet Chain-of-Thought methods reason before concluding, and logical gaps or hal…