标题： 剪枝与预测：通过共形预测优化大语言模型决策 显示英文标题

标题： Prune 'n Predict: Optimizing LLM Decision-making with Conformal Prediction

摘要： 大型语言模型（LLMs）正在增强多个应用中的决策能力，包括工具或API使用以及回答多项选择题（MCQs）。 然而，在医疗和金融等高风险领域中，错误的输出会带来重大风险。 为了量化LLM的不确定性并从而减轻这些风险，最近的研究采用了一种模型和分布无关的框架——分位数预测（CP），该框架利用LLM输出生成一个\emph{预测集}，该集合以高概率包含正确答案。 利用CP，我们提出了\emph{问题的共形修订}（CROQ），该方法通过缩小可用选项到预测集中的选项并询问LLM修改后的问题来修订问题。 我们期望LLM在选项更少的修改后问题上更加准确。 此外，我们预计当CP的预测集较小时，CROQ将更有效。 常用的logit分数通常会导致较大的集合，从而削弱CROQ的效果。 为克服这一点，我们提出了CP-OPT，这是一个优化框架，用于学习在保持覆盖率的同时最小化集合大小的分数。 我们在MMLU、ToolAlpaca和TruthfulQA数据集上进行了广泛的实验，使用多种LLM表明，CROQ在标准推理基础上提高了准确性，尤其是在与CP-OPT结合时效果更为显著。 显示英文摘要

摘要： Large language models (LLMs) are empowering decision-making in several applications, including tool or API usage and answering multiple-choice questions (MCQs). However, incorrect outputs pose significant risks in high-stakes domains like healthcare and finance. To quantify LLM uncertainty and thereby mitigate these risks, recent works employ conformal prediction (CP), a model- and distribution-agnostic framework that uses LLM outputs to generate a \emph{prediction set} containing the true answer with high probability. Leveraging CP, we propose \emph{conformal revision of questions} (CROQ), which revises the question by narrowing down the available choices to those in the prediction set and asking the LLM the revised question. We expect LLMs to be more accurate on revised questions with fewer choices. Furthermore, we expect CROQ to be effective when the prediction sets from CP are small. Commonly used logit scores often lead to large sets, diminishing CROQ's effectiveness. To overcome this, we propose CP-OPT, an optimization framework to learn scores that minimize set sizes while maintaining coverage. Our extensive experiments on MMLU, ToolAlpaca, and TruthfulQA datasets with multiple LLMs show that CROQ improves accuracy over the standard inference, with more pronounced gains when paired with CP-OPT.