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Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation
Jen-tse Huang, Chang Chen, Shiyang Lai +3
Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues. While Multimodal Large Language…
Reading, Not Thinking: Understanding and Bridging the Modality Gap When Text Becomes Pixels in Multimodal LLMs
Kaiser Sun, Xiaochuang Yuan, Hongjun Liu +4
Multimodal large language models (MLLMs) can process text presented as images, yet they often perform worse than when the same content is provided as textual tokens. We systematica…
Artificial Intolerance: Stigmatizing Language in Clinical Documentation Skews Large Language Model Decision-Making
Jen-tse Huang, Didi Zhou, Faith Kamau +5
Large Language Models (LLMs) are increasingly deployed in high-stakes domains such as clinical decision support and medical documentation. However, the robustness of these models a…
Weird Generalization is Weirdly Brittle
Miriam Wanner, Hannah Collison, William Jurayj +3
Weird generalization is a phenomenon in which models fine-tuned on data from a narrow domain (e.g. insecure code) develop surprising traits that manifest even outside that domain (…
Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict
Kaiser Sun, Fan Bai, Mark Dredze
Large language models (LLMs) draw on both contextual information and parametric memory, yet these sources can conflict. Prior studies have largely examined this issue in contextual…
Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions
Hanjie Chen, Zhouxiang Fang, Yash Singla +1
LLMs have demonstrated impressive performance in answering medical questions, such as achieving passing scores on medical licensing examinations. However, medical board exams or ge…