少样本策略（解）组合在对话式问答中的应用
Few-shot Policy (de)composition in Conversational Question Answering
AI总结：
提出LDPC神经符号框架，利用大型语言模型在少样本下通过逻辑分解检测策略合规，在ShARC基准上无需微调即达竞争性能，并增强可解释性。
摘要：
策略合规检测（PCD）的任务是判断一个场景是否符合一套书面策略。在对话环境中，PCD的结果可以指示是否需要提出澄清性问题以确定合规状态。现有方法通常声称具有潜在或需要大量标注数据的推理能力。在这项工作中，我们提出了策略合规的逻辑分解（LDPC）：一种神经符号框架，用于在少样本设置下使用大型语言模型（LLMs）检测策略合规性。通过仅选择少量示例以及最近开发的提示技术，我们证明了我们的方法能够通过提取需要回答的子问题、从上下文信息中分配真值，并明确地从给定策略中生成一组逻辑语句，来合理地推理策略合规对话。显式逻辑图的表述反过来可以帮助以更高的透明度和可解释性回答与PCD相关的问题。我们将此方法应用于流行的PCD和对话式机器阅读基准ShARC，并在无需任务特定微调的情况下展示了竞争性能。我们还利用LDPC固有的可解释架构来理解错误发生的位置，揭示了ShARC数据集中的歧义，并强调了对话式问答推理所面临的挑战。
英文摘要：
The task of policy compliance detection (PCD) is to determine if a scenario is in compliance with respect to a set of written policies. In a conversational setting, the results of PCD can indicate if clarifying questions must be asked to determine compliance status. Existing approaches usually claim to have reasoning capabilities that are latent or require a large amount of annotated data. In this work, we propose logical decomposition for policy compliance (LDPC): a neuro-symbolic framework to detect policy compliance using large language models (LLMs) in a few-shot setting. By selecting only a few exemplars alongside recently developed prompting techniques, we demonstrate that our approach soundly reasons about policy compliance conversations by extracting sub-questions to be answered, assigning truth values from contextual information, and explicitly producing a set of logic statements from the given policies. The formulation of explicit logic graphs can in turn help answer PCDrelated questions with increased transparency and explainability. We apply this approach to the popular PCD and conversational machine reading benchmark, ShARC, and show competitive performance with no task-specific finetuning. We also leverage the inherently interpretable architecture of LDPC to understand where errors occur, revealing ambiguities in the ShARC dataset and highlighting the challenges involved with reasoning for conversational question answering.
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。
