Research Progress on the Hallucination Problem of Large Language Models and Some Mitigation Strategies

Abstract

Although Large Language Models (LLMs) have made significant progress in the field of natural language processing, the hallucination phenomenon has become a core issue in judging their credibility and practicality, which may cause serious consequences, especially in high-risk fields such as medicine and law. This article systematically explains the causes and current situation of LLM hallucinations and focuses on sorting out the current mainstream methods of research on alleviating hallucinations. First, the definition and classification of hallucinations are provided, and then the generation mechanism of hallucinations is explained from various aspects, such as data deviation and training goals. Then, six effective mitigation strategies on the market are introduced in detail and with emphasis. In addition, this article also introduces the main principle of evaluation and detection effectiveness using annotation to quantify hallucination tendency, and briefly describes three practical methods. Finally, he mentioned the security challenges of fabricated or misleading information generated by AI in legal, regulatory, and governance aspects, and put forward new ideas for looking at the problem of AI illusions in a positive way when looking into future research directions. Generally speaking, the problem of hallucinations in large language models consistently exists, and completely solving the hallucination problem still faces huge challenges.

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