Aligning Probabilistic Beliefs under Informative Missingness: LLM Steerability in Clinical Reasoning
#probabilistic beliefs #informative missingness #LLM steerability #clinical reasoning #uncertainty #data alignment #AI reliability
📌 Key Takeaways
- The article discusses aligning probabilistic beliefs in clinical reasoning when data is missing but informative.
- It explores how Large Language Models (LLMs) can be steered to handle such missingness in clinical contexts.
- The focus is on improving LLM reliability and accuracy in probabilistic reasoning under uncertainty.
- The research highlights methods to guide LLMs in making better clinical decisions despite incomplete information.
📖 Full Retelling
arXiv:2512.00479v2 Announce Type: replace
Abstract: Large Language Models (LLMs) are increasingly deployed for clinical reasoning tasks, which inherently require eliciting calibrated probabilistic beliefs based on available evidence. However, real-world clinical data are frequently incomplete, with missingness patterns often informative of patient prognosis; for example, ordering a rare laboratory test reflects a clinician's latent suspicion. In this work, we investigate whether LLMs can be ste
🏷️ Themes
Clinical AI, LLM Steerability
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Original Source
arXiv:2512.00479v2 Announce Type: replace
Abstract: Large Language Models (LLMs) are increasingly deployed for clinical reasoning tasks, which inherently require eliciting calibrated probabilistic beliefs based on available evidence. However, real-world clinical data are frequently incomplete, with missingness patterns often informative of patient prognosis; for example, ordering a rare laboratory test reflects a clinician's latent suspicion. In this work, we investigate whether LLMs can be ste
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