# A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

**Authors:** Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, Ting Liu  
**arXiv:** [2311.05232](https://arxiv.org/abs/2311.05232)  
**Date:** 2023-11-09  
**Summary page:** https://www.summarizepaper.com/en/paper/2311.05232v1/

## Key points

- Large language models (LLMs) have revolutionized natural language processing (NLP), enabling significant advancements in text understanding and generation.
- A critical issue plaguing LLMs is their tendency to produce hallucinations, generating content that deviates from real-world facts or user inputs.
- There has been a growing focus on detecting and mitigating these hallucinations to address the challenges they pose in practical deployment of LLMs.
- The survey titled "A Survey on Hallucination in Large Language Models" provides an overview of recent advances in addressing LLM hallucinations, including a taxonomy of hallucinations, factors contributing to their occurrence, methods for detection, benchmarks, and approaches for mitigation.
- The survey aims to pave the way for future research by analyzing current limitations and formulating open questions related to addressing hallucinations in LLMs.
- This comprehensive survey spans 49 pages and serves as a valuable resource for researchers and practitioners seeking insights into enhancing the reliability and accuracy of LLM-generated content in NLP applications.

## Summary

The emergence of large language models (LLMs) has revolutionized natural language processing (NLP), enabling significant advancements in text understanding and generation. However, a critical issue plaguing LLMs is their tendency to produce hallucinations, generating content that deviates from real-world facts or user inputs. This phenomenon poses substantial challenges to the practical deployment of LLMs and raises concerns about their reliability in real-world scenarios. In response to this challenge, there has been a growing focus on detecting and mitigating these hallucinations. In their comprehensive survey titled "A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions," authors Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng,
Xiaocheng Feng,
Bing Qin,
and Ting Liu aim to provide an in-depth overview of recent advances in addressing LLM hallucinations. The survey begins by introducing an innovative taxonomy of LLM hallucinations and delves into the factors contributing to their occurrence. Subsequently,
the authors present a detailed overview of various methods and benchmarks for detecting hallucinations in LLM-generated content. Moreover,
the survey highlights representative approaches designed to mitigate hallucinations effectively. By analyzing the current limitations and formulating open questions in this domain,
the authors aim to pave the way for future research on addressing hallucinations in LLMs. This work is still in progress and spans 49 pages. Overall,
this survey serves as a valuable resource for researchers and practitioners seeking insights into tackling the challenges posed by hallucinations in large language models. Through its thorough examination of principles,
taxonomy,
challenges,
and open questions surrounding LLM hallucinations,
this survey contributes significantly to advancing our understanding of how to enhance the reliability and accuracy of LLM-generated content in NLP applications.