RePrompT uses recurrent prompt tuning to inject prior-visit latent states and cohort-derived population prompt tokens into LLMs, yielding better performance than pure EHR or pure LLM baselines on MIMIC clinical prediction tasks.
Calibrating verbalized probabilities for large language models, 2024 a
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Empirical study finds verbalized per-token confidence methods in LLMs for MT perform similarly to internal signals on error detection and calibration but show little correlation.
A multi-strategy interrogation method with auxiliary expert assessment reduces expected calibration error by 40% on average across three medical VQA datasets for MLLMs.
citing papers explorer
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RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models
RePrompT uses recurrent prompt tuning to inject prior-visit latent states and cohort-derived population prompt tokens into LLMs, yielding better performance than pure EHR or pure LLM baselines on MIMIC clinical prediction tasks.
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Speaking in Self-Assessing Tongues: On the Verbalized Confidence of LLMs in Machine Translation
Empirical study finds verbalized per-token confidence methods in LLMs for MT perform similarly to internal signals on error detection and calibration but show little correlation.
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Confidence Calibration for Multimodal LLMs: An Empirical Study through Medical VQA
A multi-strategy interrogation method with auxiliary expert assessment reduces expected calibration error by 40% on average across three medical VQA datasets for MLLMs.
- Process Supervision of Confidence Margin for Calibrated LLM Reasoning