Enhancing Healthcare LLM Trust with Atypical Presentations Recalibration

• URL: http://arxiv.org/abs/2409.03225v1
• Date: Thu, 5 Sep 2024 03:45:35 GMT
• Title: Enhancing Healthcare LLM Trust with Atypical Presentations Recalibration
• Authors: Jeremy Qin, Bang Liu, Quoc Dinh Nguyen,
• Abstract summary: Black-box large language models (LLMs) are increasingly deployed in various environments. LLMs often exhibit overconfidence, leading to potential risks and misjudgments. We propose a novel method, textitAtypical presentations Recalibration, which leverages atypical presentations to adjust the model's confidence estimates.
• Score: 20.049443396032423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Black-box large language models (LLMs) are increasingly deployed in various environments, making it essential for these models to effectively convey their confidence and uncertainty, especially in high-stakes settings. However, these models often exhibit overconfidence, leading to potential risks and misjudgments. Existing techniques for eliciting and calibrating LLM confidence have primarily focused on general reasoning datasets, yielding only modest improvements. Accurate calibration is crucial for informed decision-making and preventing adverse outcomes but remains challenging due to the complexity and variability of tasks these models perform. In this work, we investigate the miscalibration behavior of black-box LLMs within the healthcare setting. We propose a novel method, \textit{Atypical Presentations Recalibration}, which leverages atypical presentations to adjust the model's confidence estimates. Our approach significantly improves calibration, reducing calibration errors by approximately 60\% on three medical question answering datasets and outperforming existing methods such as vanilla verbalized confidence, CoT verbalized confidence and others. Additionally, we provide an in-depth analysis of the role of atypicality within the recalibration framework.

Related papers

• Fact-Level Confidence Calibration and Self-Correction [64.40105513819272]
  We propose a Fact-Level framework that calibrates confidence to relevance-weighted correctness at the fact level. We also develop Confidence-Guided Fact-level Self-Correction ($textbfConFix$), which uses high-confidence facts within a response as additional knowledge to improve low-confidence ones.
  arXiv  Detail & Related papers  (2024-11-20T14:15:18Z)
• On Calibration of LLM-based Guard Models for Reliable Content Moderation [27.611237252584402]
  Large language models (LLMs) pose significant risks due to the potential for generating harmful content or users attempting to evade guardrails. Existing studies have developed LLM-based guard models designed to moderate the input and output of threat LLMs. However, limited attention has been given to the reliability and calibration of such guard models.
  arXiv  Detail & Related papers  (2024-10-14T12:04:06Z)
• Confidence Estimation for LLM-Based Dialogue State Tracking [9.305763502526833]
  Estimation of a model's confidence on its outputs is critical for Conversational AI systems based on large language models (LLMs) We provide an exhaustive exploration of methods, including approaches proposed for open- and closed-weight LLMs. Our findings suggest that fine-tuning open-weight LLMs can result in enhanced AUC performance, indicating better confidence score calibration.
  arXiv  Detail & Related papers  (2024-09-15T06:44:26Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation [18.815226646364476]
  Existing calibration methods for large language models (LLMs) focus on estimating or eliciting individual confidence without taking full advantage of the "Collective Wisdom" We propose Collaborative, a post-hoc training-free calibration strategy that leverages the collaborative and expressive capabilities of multiple tool-augmented LLM agents in a simulated group deliberation process.
  arXiv  Detail & Related papers  (2024-04-14T02:40:43Z)
• Calibrating Large Language Models with Sample Consistency [76.23956851098598]
  We explore the potential of deriving confidence from the distribution of multiple randomly sampled model generations, via three measures of consistency. Results show that consistency-based calibration methods outperform existing post-hoc approaches. We offer practical guidance on choosing suitable consistency metrics for calibration, tailored to the characteristics of various LMs.
  arXiv  Detail & Related papers  (2024-02-21T16:15:20Z)
• A Study on the Calibration of In-context Learning [27.533223818505682]
  We study in-context learning (ICL), a prevalent method for adapting static language models through tailored prompts. We observe that, with an increasing number of ICL examples, models initially exhibit increased miscalibration before achieving better calibration. We explore recalibration techniques and find that a scaling-binning calibrator can reduce calibration errors consistently.
  arXiv  Detail & Related papers  (2023-12-07T03:37:39Z)
• On the Calibration of Large Language Models and Alignment [63.605099174744865]
  Confidence calibration serves as a crucial tool for gauging the reliability of deep models. We conduct a systematic examination of the calibration of aligned language models throughout the entire construction process. Our work sheds light on whether popular LLMs are well-calibrated and how the training process influences model calibration.
  arXiv  Detail & Related papers  (2023-11-22T08:57:55Z)
• Calibrating Multimodal Learning [94.65232214643436]
  We propose a novel regularization technique, i.e., Calibrating Multimodal Learning (CML) regularization, to calibrate the predictive confidence of previous methods. This technique could be flexibly equipped by existing models and improve the performance in terms of confidence calibration, classification accuracy, and model robustness.
  arXiv  Detail & Related papers  (2023-06-02T04:29:57Z)
• Calibration of Neural Networks [77.34726150561087]
  This paper presents a survey of confidence calibration problems in the context of neural networks. We analyze problem statement, calibration definitions, and different approaches to evaluation. Empirical experiments cover various datasets and models, comparing calibration methods according to different criteria.
  arXiv  Detail & Related papers  (2023-03-19T20:27:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.