Representative Chain-of-Reasoning Framework for Aspect Sentiment Quad Prediction

Aspect Sentiment Quad Prediction ASQP is a crucial sentiment analysis task that has attracted increasing attention. The most recent studies focus on generating complete sentiment quadruples through end-to-end generative models. However, thesemethods heavily depend on labeled data quality and quantity, performing poorly in low-resource scenarios and less suitable for real-world applications. To address these issues, we propose a novel Representative Chain-of-Reasoning framework RCR , with the aim of providing representative knowledge for large language models LLMs and fully activating their reasoning capabilities for ASQP. Specifically, we develop a Chain Prompting ChaPT module to decompose the ASQP task into three subtasks using the step-by-step reasoning mechanism. Then, a Representative Demonstration Retriever RepDR is introduced to provide ChaPT with representative demonstrations, balancing diversity and similarity, and enhancing the reasoning capabilities of LLMs at each step. Experimental results demonstrate the superiority of RCR in low-resource scenarios, with its optimal performance even surpassing that of the fully supervised BERT baseline.