Strategies to mitigate hallucinations in large language models

Ranjeeta Bhattacharya
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Ranjeeta Bhattacharya: Senior Data Scientist, BNY Mellon AI Hub, USA

Applied Marketing Analytics: The Peer-Reviewed Journal, 2024, vol. 10, issue 1, 62-67

Abstract: In the world of enterprise-level applications, the construction and utilisation of large language models (LLMs) carry a paramount significance, accompanied by the crucial task of mitigating hallucinations. These instances of generating factually inaccurate information pose challenges during both the initial development phase of LLMs and the subsequent refinement process through prompt engineering. This paper delves into a variety of approaches such as retrieval augmented generation, advanced prompting methodologies, harnessing the power of knowledge graphs, construction of entirely new LLMs from scratch etc, aimed at alleviating these challenges. The paper also underscores the indispensable role of human oversight and user education in addressing this evolving issue. As the field continues to evolve, the importance of continuous vigilance and adaptation cannot be overstated, with a focus on refining strategies to effectively combat hallucinations within LLMs.

Keywords: LLM; large language model; hallucination; prompt engineering; RAG (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2024
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