The remarkable capabilities of large language models make them increasingly compelling for use in real-world healthcare applications. However, the risks associated with using these artificial intelligence systems in medicine are not systematically understood. The aim of this study is to characterize these risks b...
Yi-Fan Yang, Qiao Jin, Robert Leaman et al.· Communications Medicine· 0 citations
Concept-Residual eXpansion (CRX), a concept-augmented framework that improves robustness by expanding the set of candidate predictive features by improving robustness to spurious correlations, is proposed.
Eric Xie, Guang-Zhi Xiong, Wenqian Ye et al.· Proceedings of the 32nd ACM...· 0 citations
InfRL (Inference-time Reinforcement Learning) offers a practical and domain-agnostic approach to harness reinforcement learning during inference, bridging the gap between static prompting and computationally intensive parameter-level fine-tuning.
Sikun Guo, Amir Hassan Shariatmadari, Jiuqi Wang et al.· Proceedings of the 32nd ACM...· 0 citations
Scientific ideation is driven by curiosity: researchers ask questions that expose knowledge gaps, reveal competing hypotheses, and clarify missing evidence relevant to decision making. Yet, most LLM-based ideation systems optimize the idea text while leaving curiosity under-modeled, resulting in brittle, engine-specifi...
Sikun Guo, Di Wang, Xiaohan Fan et al.· Proceedings of the 32nd ACM...· 0 citations
Large language models (LLMs) possess extensive latent knowledge yet remain largely static at inference. Once prompted, their generation policy typically cannot evolve, and post-hoc ''self-reflection'' methods provide no explicit principled learning signals. To address this limitation, we formally model iterative resear...
Sikun Guo, Amir Hassan Shariatmadari, Jiuqi Wang et al.· Proceedings of the 32nd ACM...· 0 citations
Concept-Residual eXpansion (CRX), a concept-augmented framework that improves robustness by expanding the set of candidate predictive features by improving robustness to spurious correlations, is proposed.
Eric Xie, Guangzhi Xiong, Wenqian Ye et al.· Proceedings of the 32nd ACM...· 0 citations
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