--- title: "Who cites Cancer-Myth: Evaluating Large — 5 citing papers" description: "5 papers of the Elamur Research library cite «Cancer-Myth: Evaluating Large Language Models on Patient Questions with False…" canonical: "https://research.elamur.ai/free/en/papers/zhu-2025-cancer-myth-evaluating-large-language/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhu-2025-cancer-myth-evaluating-large-language/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Cancer-Myth: Evaluating Large Language Models on…](https://md.research.elamur.ai/free/en/papers/zhu-2025-cancer-myth-evaluating-large-language/) / cited by # Who cites «Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions» 5 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions»](https://md.research.elamur.ai/free/en/papers/zhu-2025-cancer-myth-evaluating-large-language/) 1. [**Don't ‘Well, Actually' Me Unless You Know What You're Talking About: Weak Presupposition Verification Degrades General QA Performance arXiv:2608.06539**Shenran Wang, Vered Shwartz, Hila Gonen*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-don-t-well-actually-me/) 2. **[MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication](https://md.research.elamur.ai/free/en/papers/sambara-2026-medredflag-investigating-how-llms-redirect/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sraavya Sambara, Yuan Pu, Ayman Ali, et al.*2026* 3 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zhu-2025-cancer-myth-evaluating-large-language/) — Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions