Cross-target stance detection (CTSD) is an important task, which infers the attitude of the destination target by utilizing annotated data derived from the source target. One important approach in CTSD is to extract domain-invariant features to bridge the knowledge gap between multiple targets. However, the analysis of informal and short text structure, and implicit expressions, complicate the extraction of domain-invariant knowledge. In this paper, we propose a Multi-Perspective Prompt-Tuning (MPPT) model for CTSD that uses the analysis perspective as a bridge to transfer knowledge. First, we develop a two-stage instruct-based chain-of-thought method (TsCoT) to elicit target analysis perspectives and provide natural language explanations (NLEs) from multiple viewpoints by formulating instructions based on large language model (LLM). Second, we propose a multi-perspective prompt-tuning framework (MultiPLN) to fuse the NLEs into the stance predictor. Extensive experiments results demonstrate the superiority of MPPT against the state-of-the-art baseline methods.

翻译：跨目标立场检测（CTSD）是一项重要任务，旨在利用源目标标注数据推断目标对象的态度。该任务的核心方法之一是提取领域不变特征以弥合多目标间的知识鸿沟。然而，非正式短文本结构与隐式表达的分析使领域不变知识的提取面临挑战。本文提出一种面向CTSD的多视角提示微调（MPPT）模型，通过分析视角作为知识迁移的桥梁。首先，我们开发基于指令的两阶段思维链方法（TsCoT），借助大语言模型（LLM）构建指令，从多角度诱发目标分析视角并生成自然语言解释（NLEs）。其次，提出多视角提示微调框架（MultiPLN），将NLEs融合至立场预测器中。大量实验结果表明，MPPT方法相比当前最优基线方法具有显著优越性。