Enhancing Healthcare LLM Trust with Atypical Presentations Recalibration (2024.findings-emnlp)
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| Challenge: | Existing methods for eliciting and calibrating large language models have focused on general reasoning datasets, yielding only modest improvements. |
| Approach: | They propose a method which leverages atypical presentations to adjust model confidence estimates. |
| Outcome: | The proposed method reduces calibration errors by approximately 60% on three medical question answering datasets and outperforms existing methods such as vanilla verbalized confidence, CoT verbalised confidence and others. |
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| Challenge: | Existing methods for clinical code verification fail to account for hierarchical misalignments . standardized coding systems such as ICD-10-CM1 ensure consistency across medical records. |
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Parisa Rabbani, Priyam Sahoo, Ruben Mathew, Aishee Mondal, Harshita Ketharaman, Nimet Beyza Bozdag, Dilek Hakkani-Tür
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Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback (2023.emnlp-main)
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Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, Christopher Manning
| Challenge: | Recent studies have shown that unsupervised pre-training produces large language models whose conditional probabilities are remarkably well-calibrated. |
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| Challenge: | Recent advances in Large Language Models have brought significant improvements to various service domains, including chatbots and medical pre-consultation applications. |
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| Challenge: | Language models (LMs) have significant potential for clinical prediction tasks . however, unreliable decisions can result in significant costs due to compromised patient safety and ethical concerns . |
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Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates (2022.emnlp-main)
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| Challenge: | Existing methods to improve confidence calibration of pre-trained language models are still a mystery. |
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A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare (2025.findings-emnlp)
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| Challenge: | a survey of large language models in healthcare raises critical concerns around trustworthiness . trustworthy of LLMs in healthcare remains underexplored, lacking a systematic review . |
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Knowing When to Abstain: Medical LLMs Under Clinical Uncertainty (2026.eacl-long)
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Sravanthi Machcha, Sushrita Yerra, Sahil Gupta, Aishwarya Sahoo, Sharmin Sultana, Hong Yu, Zonghai Yao
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Large Language Models Are Overconfident in Their Own Responses (2026.findings-acl)
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| Challenge: | Prior work has shown that instruction-tuned large language models are less well calibrated than their base pre-trained counterparts. |
| Approach: | They propose a simple inference-time strategy that frams the model’s answer as user input during confidence elicitation. |
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