A retrieval-augmented multiagent system for financial sentiment analysis

Du, K., Zhao, Y., Mao, R., Xing, F. ORCID: https://orcid.org/0000-0002-5751-3937 and Cambria, E. (2025) A retrieval-augmented multiagent system for financial sentiment analysis. IEEE Intelligent Systems, 40 (2). pp. 15-22. ISSN 1941-1294 doi: 10.1109/MIS.2025.3544912

Abstract/Summary

Financial sentiment analysis (FSA) has seen substantial advancements with the use of large language models (LLMs). Previous research highlighted the effectiveness of retrieval-augmented generation (RAG) and multiagent LLMs for FSA as these approaches alleviate the problems of hallucination, a lack of factual knowledge, and limited complex problem-solving capability. Despite this, the interplay and potential synergies between these two methods remain largely unexplored. This study presents a notable leap forward by introducing a retrieval-augmented multiagent system (RAMAS) to enhance LLM-based FSA performance. An RAMAS is specifically designed to deepen understanding of the critical factors that are inherent in FSA and mimic human-like consensus-making processes by adaptively learning from semantically similar few-shot samples and engaging in conversations among the generator, discriminator, and arbitrator agents. Our evaluation of RAMASs demonstrates improved accuracy and F1-score across multiple established FSA benchmark datasets.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/123573
Identification Number/DOI	10.1109/MIS.2025.3544912
Refereed	No
Divisions	Henley Business School > Digitalisation, Marketing and Entrepreneurship
Publisher	IEEE
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