# A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

**Authors:** S. M Towhidul Islam Tonmoy, S M Mehedi Zaman, Vinija Jain, Anku Rani, Vipula Rawte, Aman Chadha, Amitava Das  
**arXiv:** [2401.01313](https://arxiv.org/abs/2401.01313)  
**Date:** 2024-01-02  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.01313v3/

## Key points

- Generated content appears factual but lacks grounding
- Poses a significant challenge to safe deployment in real-world applications
- Methods employed after generation and end-to-end approaches
- Automated attribution process aligning content with evidence (with preserved original qualities)
- Utilizing open-source LLMs to detect and replace high entropy words
- Integration of pre-trained sequence-to-sequence transformer with dense vector index of Wikipedia via Dense Passage Retriever (DPR)
- Integrates knowledge acquisition and answer generation
- Improves factuality, consistency, and entailment of generated answers
- Leverages interactivity and multitasking abilities of LLMs for more precise and accurate answers
- Comprehensive survey on over 32 techniques developed to address hallucination issues in LLMs:
- Categorized based on dataset utilization, common tasks, feedback mechanisms, and retriever types
- Paper provides analysis of challenges and limitations inherent in these techniques:
- Establishes a foundation for future research aimed at enhancing reliability of LLM outputs

## Summary

In the realm of , the issue of , where generated content appears factual but lacks grounding, poses a significant challenge to their safe deployment in real-world applications. This paper explores various techniques developed to mitigate hallucination in LLMs, focusing on methods employed after generation and end-to-end approaches. One notable technique discussed is , which automates the attribution process for text generation models by aligning content with retrieved evidence while preserving original qualities. Another approach, , involves utilizing open-source LLMs to detect and replace high entropy words, reducing hallucinations in generated content. The paper also delves into , which integrates a pre-trained sequence-to-sequence transformer with a dense vector index of Wikipedia accessed through the Dense Passage Retriever (DPR). This innovative combination allows the model to generate output conditioned on both input queries and latent documents provided by the DPR, effectively reducing hallucinations in generated text. Furthermore, the paper introduces an interactive self-reflection methodology that integrates knowledge acquisition and answer generation to improve factuality, consistency, and entailment of generated answers. Leveraging the interactivity and multitasking abilities of LLMs, this approach produces more precise and accurate answers while reducing hallucinations compared to baselines. Overall, this comprehensive survey highlights over 32 techniques developed to address hallucination issues in LLMs, categorizing them based on dataset utilization, common tasks, feedback mechanisms, and retriever types. By analyzing challenges and limitations inherent in these techniques, this paper provides a solid foundation for future research aimed at enhancing the reliability of LLM outputs in practical settings.