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Chitta Baral

As seen in: Apple Podcasts, Nature, arXiv, Communications of the ACM
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Articles

Structured reasoning failures compromise LLM interpretation of clinical oncology notes

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By Matthew W. Kenaston, Umair Ayub, Mihir Parmar, Priya Kumar, Muhammad Umair Anjum, Aadel A. Chaudhuri, Yousef Zakharia, Elisabeth I. Heath, Tanios S Bekaii-Saab, Cui Tao, Eliezer Van Allen, Yoojung Choi, Chitta Baral, Irbaz Bin Riaz
| Nature Verified
Abstract Large language models (LLMs) show strong performance on clinical benchmarks, yet their reasoning reliability in real-world oncology care remains unclear. We evaluated LLM reasoning on authentic oncology notes using a novel hierarchical error taxonomy across two retrospective cohorts spanning breast, pancreatic, and prostate cancer. GPT-4 produced reasoning errors in 23.1% of note interpretations, the majority reflecting cognitive bias patterns.
Open in Who Shared Issue with article?

[2512.15949] The Perceptual Observatory Characterizing Robustness and Grounding in MLLMs

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By Fenil Bardoliya, Chitta Baral, Vivek Gupta
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (418) Issue with article?

RefEdit: A Benchmark and Method for Improving Instruction-based Image Editing Model on Referring Expressions

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By Maitreya Patel, Shivam Singh, Yezhou Yang, Chitta Baral
| arXiv
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