arXiv AI By Sadif Ahmed, Soumya Swagata Biswas, Rifat Shahriyar, Chowdhury Mohammad Rakin Haider

Seeing Through the Forgery Is Not Enough: Regressive Sycophancy under Fabricated Evidence in Medical Vision-Language Models

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arXiv AI
Sep 3

Untangling the Mechanisms of Misleading Context in Medical Question Answering

The paper investigates how misleading context—specifically fabricated evidence and bare assertions—affects large language models’ medical question‑answering performance. Experiments on MedMisBench show that models are more prone to adopt answers based on assertions than fabricated evidence, and that these misleading cues are often disclosed in reasoning traces but rarely in final responses. A monitor that reads open reasoning traces can detect most corrupted decisions, whereas monitoring only responses is less effective.

By Robin Linzmayer, No\'emie Elhadad
arXiv AI
Aug 28

MedFabric: Gold Evidence Hides the Difficulty of Word-Level Medical Fabrication Detection

MedFabric is a new benchmark for detecting word‑level medical fabrications, comprising 646 fabricated statements each paired with a ground‑truth passage that shares the same LLM authorship and nearly identical wording. The study shows that current detectors perform poorly—expert clinicians achieve only 53.3% macro‑F1 and no detector family surpasses 60% without gold evidence—highlighting that detection hinges on evidence correctness rather than subtlety of fabrication. The authors demonstrate that a retrieval‑confidence gate can substantially improve performance, raising macro‑F1 from 61% to 74%.

By Tung Sum Thomas Kwok, Qian Qian, Xiaofeng Lin, Dongxu Zhang, Jun Han, Zhichao Yang, Davin Hill, Tamer Soliman, Sanjit Singh Batra, Robert Tillman, Guang Cheng