Multi-Chain of Thought Prompt Learning for Aspect-Based Sentiment Analysis

Abstract

Due to their extensive common-sense knowledge and linguistic understanding, large language models (LLMs) have demonstrated remarkable capabilities in text comprehension and logical reasoning for natural language processing tasks. Traditional prompt-based learning methods, which rely on contextual pattern matching, have proven to be effective in extracting knowledge from LLMs. However, these approaches are constrained by training data pattern matching, overlook reasoning processes, and consequently suffer from suboptimal prompt performance and limited interpretability. Moreover, considering that the intermediate steps generated by single-chain reasoning may not effectively assist LLMs in identifying the sentiment polarity of aspect terms, and that multiple reasoning paths often exist for complex reasoning tasks to reach correct conclusions, this paper proposes a Multi-Chain Thought Prompt Learning framework (MT-CPL). Starting from fundamental concepts, this method simulates human multi-path reasoning patterns to progressively construct comprehensive thought processes and deeply explore sentiment cues. Based on syntactic structures and the semantic logic of text, the framework incorporates four distinct perspectives of text comprehension: hierarchical reading, experiential reading, keyword-based reading, and analogical reading. It establishes a multi-chain prompt template and employs voting mechanisms to select correct reasoning path outcomes. The MT-CPL approach aims to guide LLMs in mining multi-dimensional textual information from different perspectives, gradually uncovering hidden contextual sentiment clues, while mitigating issues caused by irrelevant sentiment cues in intermediate reasoning steps. By decomposing main tasks incrementally, the method achieves progressive reasoning, effectively reduces the difficulty of direct analysis, and further enhances model interpretability through the integration of inherent common-sense knowledge.