Large Language Model Enhanced Logic Tensor Network for Stance Detection.

Journal: Neural networks : the official journal of the International Neural Network Society
PMID:

Abstract

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.

Authors

  • Genan Dai
    College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China; Guangdong Key Laboratory for Intelligent Computation of Public Service Supply, China.
  • Jiayu Liao
    Department of Bioengineering, Bourns College of Engineering, University of California at Riverside, Riverside, CA, USA.
  • Sicheng Zhao
  • Xianghua Fu
    College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China. Electronic address: fuxianghua@sztu.edu.cn.
  • Xiaojiang Peng
    College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
  • Hu Huang
  • Bowen Zhang
    HUB of Intelligent Neuro-Engineering (HUBIN), Aspire CREATe, DSIS, University College London, London, HA7 4LP, UK.