IDEA: 一种通过语音到数值校准实现LLM可解释和可编辑决策框架
IDEA: An Interpretable and Editable Decision-Making Framework for LLMs via Verbal-to-Numeric Calibration
AI总结：
IDEA通过EM联合学习、相关采样和数学保证的参数编辑，实现LLM决策的校准概率与量化人机协作，实验显示其在五个数据集上优于其他模型。
摘要：
大型语言模型越来越多地用于决策，但其在高风险领域应用受限于概率校准不足、解释不忠实和无法精确整合专家知识。我们提出IDEA框架，将LLM决策知识提取为可解释的参数模型，通过EM联合学习语音到数值映射、决策参数，相关采样保持因素依赖性，以及数学保证的直接参数编辑，实现校准概率并支持量化人机协作。在五个数据集上实验显示，IDEA使用Qwen-3-32B（78.6%）优于DeepSeek R1（68.1%）和GPT-5.2（77.9%），实现完美因素排除和精确校准——通过提示无法达到的精度。实现代码已公开在https://github.com/leonbig/IDEA。
英文摘要：
Large Language Models are increasingly deployed for decision-making, yet their adoption in high-stakes domains remains limited by miscalibrated probabilities, unfaithful explanations, and inability to incorporate expert knowledge precisely. We propose IDEA, a framework that extracts LLM decision knowledge into an interpretable parametric model over semantically meaningful factors. Through joint learning of verbal-to-numerical mappings and decision parameters via EM, correlated sampling that preserves factor dependencies, and direct parameter editing with mathematical guarantees, IDEA produces calibrated probabilities while enabling quantitative human-AI collaboration. Experiments across five datasets show IDEA with Qwen-3-32B (78.6%) outperforms DeepSeek R1 (68.1%) and GPT-5.2 (77.9%), achieving perfect factor exclusion and exact calibration -- precision unattainable through prompting alone. The implementation is publicly available at https://github.com/leonbig/IDEA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
