Title: Generating empathetic responses via knowledge augmentation and chain-of-thought in conversational information retrieval systems

Authors: Zhinan Gou; Yan Li; Mengyao Jia; Siyu Liu

Addresses: School of Management Science and Information Engineering, Hebei University of Economics and Business, Shijiazhuang, China ' School of Civil Engineering and Architecture, Hebei University of Engineering Science, Shijiazhuang, China ' School of Management Science and Information Engineering, Hebei University of Economics and Business, Shijiazhuang, China ' School of Management Science and Information Engineering, Hebei University of Economics and Business, Shijiazhuang, China

Abstract: Conversational information retrieval systems stand as a crucial research area at the intersection of information retrieval and artificial intelligence. However, existing research largely neglects the generation of emotional responses. This paper proposes a novel framework for generating empathetic responses by integrating knowledge augmentation and chain-of-thought reasoning. Firstly, we leverage the knowledge-augmented capabilities of the large language model to generate semantically similar questions, acting as supplementary data to expand the coverage of dataset and enrich the contextual diversity. Then, relevant top answers are retrieved using the original and semantically similar questions, with semantic similarity from datasets as the key metric. Moreover, we establish a chain of thought that guides the large language models to generate empathetic responses, enabling them to offer targeted suggestions for alleviating emotions associated with questions of users. Experimental results show that our method significantly outperforms the baselines in terms of emotional expression and user-perceived empathy.

Keywords: empathetic response; knowledge augmentation; chain-of-thought; large language model.

DOI: 10.1504/IJCSE.2026.155100

International Journal of Computational Science and Engineering, 2026 Vol.29 No.4, pp.333 - 343

Received: 26 Jun 2025
Accepted: 24 Nov 2025
Published online: 27 Jul 2026 *

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