Dai Genan, Liao Jiayu, Zhao Sicheng, Fu Xianghua, Peng Xiaojiang, Huang Hu, Zhang Bowen
College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China; Guangdong Key Laboratory for Intelligent Computation of Public Service Supply, China.
College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
Neural Netw. 2025 Mar;183:106956. doi: 10.1016/j.neunet.2024.106956. Epub 2024 Nov 29.
Social media platforms, rich in user-generated content, offer a unique perspective on public opinion, making stance detection an essential task in opinion mining. However, traditional deep neural networks for stance detection often suffer from limitations, including the requirement for large amounts of labeled data, uninterpretability of prediction results, and difficulty in incorporating human intentions and domain knowledge. This paper introduces the First-Order Logic Aggregated Reasoning framework (FOLAR), an innovative approach that integrates first-order logic (FOL) with large language models (LLMs) to enhance the interpretability and efficacy of stance detection. FOLAR comprises three key components: a Knowledge Elicitation module that generates FOL rules using a chain-of-thought prompting method, a Logic Tensor Network (LTN) that encodes these rules for stance detection, and a Multi-Decision Fusion mechanism that aggregates LTNs' outputs to minimize biases and improve robustness. Our experiments on standard benchmarks demonstrate the effectiveness of FOLAR, showing it as a promising solution for explainable and accurate stance detection. The source code will be made publicly available to foster further research.
社交媒体平台富含用户生成的内容，能提供关于公众舆论的独特视角，这使得立场检测成为观点挖掘中的一项重要任务。然而，传统的用于立场检测的深度神经网络常常存在局限性，包括需要大量标注数据、预测结果缺乏可解释性，以及难以纳入人类意图和领域知识。本文介绍了一阶逻辑聚合推理框架（FOLAR），这是一种创新方法，它将一阶逻辑（FOL）与大语言模型（LLMs）相结合，以提高立场检测的可解释性和有效性。FOLAR由三个关键组件组成：一个知识提取模块，它使用思维链提示方法生成FOL规则；一个逻辑张量网络（LTN），它对这些规则进行编码以进行立场检测；以及一个多决策融合机制，它聚合LTN的输出以最小化偏差并提高鲁棒性。我们在标准基准上的实验证明了FOLAR的有效性，表明它是一种用于可解释且准确的立场检测的有前途的解决方案。源代码将公开提供，以促进进一步的研究。
