# A Survey of Hallucination in Large Foundation Models

**Authors:** Vipula Rawte, Amit Sheth, Amitava Das  
**arXiv:** [2309.05922](https://arxiv.org/abs/2309.05922)  
**Date:** 2023-09-12  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.05922v1/

## Key points

- Hallucination in a Foundation Model (FM) refers to the generation of content that deviates from factual reality or includes fabricated information.
- Large Foundation Models (LFMs) are smaller, open-source LLMs with fewer parameters that often experience significant hallucination issues compared to their larger counterparts.
- The paper classifies various types of hallucination phenomena specific to LFMs and establishes evaluation criteria for assessing the extent of hallucination.
- Existing strategies for mitigating hallucination in LFMs include using external knowledge through interactive question-knowledge alignment and dehallucinating LLMs using formal methods guided by iterative prompting.
- Risks associated with LFMs can be mitigated by drawing parallels with web systems, such as incorporating elements like "citation" to improve content transparency and verifiability.
- Challenges related to multilingual LFMs are highlighted, including the generation of hallucinated translations when deployed in large-scale multilingual machine translation systems.
- The paper presents a comprehensive analysis conducted on both conventional neural machine translation models and ChatGPT, a versatile LLM that can be prompted for translation, covering various conditions including over 100 translation directions, resource levels, and languages beyond English-centric pairs.
- Future directions for addressing the hallucination challenge in LFMs include automated evaluation methods and incorporating curated sources of knowledge.

## Summary

Hallucination in a Foundation Model (FM) refers to the generation of content that deviates from factual reality or includes fabricated information. This survey paper provides an extensive overview of recent efforts that aim to identify, elucidate, and tackle the problem of hallucination with a particular focus on "Large" Foundation Models (LFMs). LFMs are smaller, open-source LLMs with fewer parameters that often experience significant hallucination issues compared to their larger counterparts. The paper classifies various types of hallucination phenomena that are specific to LFMs and establishes evaluation criteria for assessing the extent of hallucination. It also examines existing strategies for mitigating hallucination in LFMs and discusses potential directions for future research in this area. The risks associated with LFMs can be mitigated by drawing parallels with web systems such as incorporating elements like "citation" to improve content transparency and verifiability. The paper explores different approaches for mitigating hallucination in LFMs. One approach is using external knowledge through interactive question-knowledge alignment which helps assess the extent of hallucinations in LLMs. Another approach involves dehallucinating LLMs using formal methods guided by iterative prompting aiming to reduce the generation of inaccurate or hallucinated information. Additionally, the paper highlights the challenges related to multilingual LLMs. Large-scale multilingual machine translation systems have shown impressive capabilities but can generate hallucinated translations when deployed. Existing research on hallucinations has mainly focused on small bilingual models for high-resource languages leaving a gap in understanding hallucinations in massively multilingual models across diverse translation scenarios. To address this gap, the paper presents a comprehensive analysis conducted on both conventional neural machine translation models and ChatGPT, a versatile LLM that can be prompted for translation. The investigation covers various conditions including over 100 translation directions resource levels and languages beyond English-centric pairs. In conclusion, this survey paper offers a comprehensive examination of the challenges and solutions related to hallucination in LFMs. It provides insights into the classification detection mitigation strategies tasks datasets and evaluation metrics associated with hallucination in LFMs. The paper also suggests future directions for addressing the hallucination challenge in LFMs including automated evaluation methods and incorporating curated sources of knowledge.