Siren's song in the AI ocean: A survey on hallucination in large language models

While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge. This phenomenon poses a substantial challenge to the reliability of LLMs in real-world scenarios. In this paper, we survey recent efforts on the detection, explanation, and mitigation of hallucination, with an emphasis on the unique challenges posed by LLMs. We present taxonomies of the LLM hallucination phenomena and evaluation benchmarks, analyze existing approaches aiming at mitigating LLM hallucination, and discuss potential directions for future research.

2.2. What is LLM hallucination

We argue that the definition appears to have considerably expanded due to the versatility of LLMs. To this end, we categorize hallucination within the context of LLMs as follows:

• Input-conflicting hallucination, where LLMs generate content that deviates from the source input provided by users;
• Context-conflicting hallucination, where LLMs generate content that conflicts with previously generated information by itself;
• Fact-conflicting hallucination, where LLMs generate content that is not faithful to established world knowledge.