通过抽象论证增强语言模型的冲突解决能力
Enhancing Conflict Resolution in Language Models via Abstract Argumentation
AI总结：
针对大语言模型难以处理信息不完整/不一致引发冲突的问题，本文结合抽象论证框架构建带计算过程解释的数据集微调模型，提升了冲突解决泛化准确率与模型透明度。
摘要：
近年来，大语言模型（LLMs）在构建类人化、高交互性对话系统方面取得了显著进展。但在共识构建、说服等任务中，LLMs往往难以解决由信息不完整或不一致引发的冲突，暴露出其在现实应用中的局限性。针对这些缺陷，专门用于解决冲突与不一致性的逻辑框架——抽象论证（abstract argumentation）具备极高的应用价值。本文旨在通过利用形式化抽象论证，将语言模型学习与符号计算相结合，提升LLMs的冲突解决能力。为此，我们构建并整理了一个包含多样化抽象论证框架的数据集，附带论证可接受性计算过程的详细解释。随后，我们在该数据集上对LLMs进行微调，聚焦抽象冲突解决任务。作为对比基准，我们还采用思维链方法对LLMs进行评估，但该方法无法有效解决基于冲突的论证问题。实验表明，过程解释在学习中起到关键作用：相较于仅使用问答对训练的模型，结合解释训练的模型展现出更优的泛化准确率。此外，借助LLMs的自解释能力，我们的方法可提供详细说明，缓解神经网络普遍存在的透明度不足问题。
英文摘要：
In recent years, large language models (LLMs) have made significant advancements in developing human-like and engaging dialogue systems. However, in tasks such as consensus-building and persuasion, LLMs often struggle to resolve conflicts arising from incomplete or inconsistent information, revealing their limitations in real-world applications. Given these limitations, abstract argumentation, a specialized logical framework designed to resolve conflicts and inconsistencies, becomes particularly relevant. In this paper, we aim to enhance the conflict-solving capabilities of LLMs by leveraging formal abstract argumentation, integrating language model learning with symbolic computation. To achieve this, we develop and curate a dataset comprising diverse abstract argumentation frameworks, accompanied by detailed explanations of the argument acceptability computation process. Subsequently, we fine-tune LLMs on this dataset, focusing on abstract conflict resolution tasks. As a comparative baseline, LLMs are also evaluated using a chain-of-thought approach, however, they fail to solve the conflict-based arguments effectively. Our experiments demonstrate that process explanations play a crucial role in learning. Models trained with explanations exhibit superior generalization accuracy compared to those trained solely on question-answer pairs. Furthermore, leveraging LLMs' self-explanation capabilities, our approach provides detailed illustrations that mitigate the lack of transparency typically associated with neural networks.
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。
