关键词: 事实核查, 自然语言处理, 大语言模型, 提示工程, 立场感知, 多头注意力机制

Abstract:

To address the issues of evidence stance imbalance and neglect of stance information in the field of Fact-Checking （FC）， a Large Language Model-driven Stance-Aware fact-checking （LLM-SA） method was proposed. Firstly， a series of dialectical claims that differed from the original claim were generated by using a large language model， to capture different perspectives for fact-checking. Secondly， through semantic similarity calculations， the relevances of each piece of evidence sentence to the original claim and the dialectical claim were separately assessed， and the top k sentences with the highest semantic similarity to each of them were selected as the evidence to either support or oppose the original claim， which obtained evidences representing different stances， and helped the fact-checking model integrate information from multiple perspectives and evaluate the veracity of the claim more accurately. Finally， the BERT-StuSE （BERT-based Stance-infused Semantic Encoding network） model was introduced to fully incorporate the semantic and stance information of the evidence through the multi-head attention mechanism and make a more comprehensive and objective judgment on the relationship between the claim and the evidence. The experimental results on the CHEF dataset show that， compared to the BERT method， the Micro F1 value and Macro F1 value of the proposed method on the test set were improved by 3.52 and 3.90 percentage points， respectively， achieving a good level of performance. The experimental results demonstrate the effectiveness of the proposed method， and the significance of considering evidence from different stances and leveraging the stance information of the evidence for enhancing fact-checking performance.

Key words: Fact-Checking (FC), Natural Language Processing (NLP), Large Language Model (LLM), prompt engineering, stance awareness, multi-head attention mechanism