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Reading list of hallucination in LLMs. Check out our new survey paper: "Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Models"
| Date | Stars |
|---|---|
| 2026-07-31 | 1085 |
| 2026-08-04 | 1085 |
| 2026-08-06 | 1085 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# llm-hallucination-survey
<img src="https://img.shields.io/badge/Version-1.0-blue.svg" alt="Version">
<img src="https://img.shields.io/github/stars/HillZhang1999/llm-hallucination-survey?color=yellow" alt="Stars">
<img src="https://img.shields.io/github/issues/HillZhang1999/llm-hallucination-survey?color=red" alt="Issues">
`Hallucination` refers to the generated content that while seemingly plausible, deviates from user input (_input-conflicting_), previously generated context (_context-conflicting_), or factual knowledge (_fact-conflicting_).
<div align="center">
<img src="figures/hallucination_example.png" alt="LLM evaluation" width="300"><br>
</div></br>
This issue significantly undermines the reliability of LLMs in real-world scenarios.
## 📰News
😎 We have uploaded a comprehensive survey about the hallucination issue within the context of large language models, which discussed the evaluation, explanation, and mitigation. Check it out!
[Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://arxiv.org/abs/2309.01219)
If you think our survey is helpful, please kindly cite our paper:
```
@article{zhang2023hallucination,
title={Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models},
author={Zhang, Yue and Li, Yafu and Cui, Leyang and Cai, Deng and Liu, Lemao and Fu, Tingchen and Huang, Xinting and Zhao, Enbo and Zhang, Yu and Xu, Chen and Chen, Yulong and Wang, Longyue and Luu, Anh Tuan and Bi, Wei and Shi, Freda and Shi, Shuming},
journal={arXiv preprint arXiv:2309.01219},
year={2023}
}
```
## 🚀Table of Content
- [LLM-Hallucination-Survey ](#llm-hallucination-survey)
- [News](#news)
- [Table of Content](#table-of-content)
- [Evaluation](#evaluation-of-llm-hallucination)
- [Source](#source-of-llm-hallucination)
- [Mitigation](#mitigation-of-llm-hallucination)
- [Contact](#contact)
## 🔍Evaluation of LLM Hallucination
### Input-conflicting Hallucination
This kind of hallucination denotes the model response deviates from the *user input*, including task instruction and task input. This kind of hallucination has been widely studied in some traditional NLG tasks, such as:
+ `Machine Translation`:
+ **Hallucinations in Neural Machine TranslationDownload**
*Katherine Lee, Orhan Firat, Ashish Agarwal, Clara Fannjiang, David Sussillo* [[paper]](https://openreview.net/forum?id=SkxJ-309FQ) 2018.9
+ **Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation**
*Nuno M. Guerreiro, Elena Voita, André F.T. Martins* [[paper]](https://arxiv.org/abs/2208.05309) 2022.8
+ **Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better**
*David Dale, Elena Voita, Loïc Barrault, Marta R. Costa-jussà* [[paper]](https://arxiv.org/abs/2212.08597) 2022.12
+ **HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation**
*David Dale, Elena Voita, Janice Lam, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi, Cynthia Gao, Loïc Barrault, Marta R. Costa-jussà* [[paper]](https://arxiv.org/abs/2305.11746) 2023.05
+ `Data-to-text`:
+ **Controlling Hallucinations at Word Level in Data-to-Text Generation**
*Clément Rebuffel, Marco Roberti, Laure Soulier, Geoffrey Scoutheeten, Rossella Cancelliere, Patrick Gallinari*[[paper]](https://arxiv.org/abs/2102.02810) 2021.2
+ **On Hallucination and Predictive Uncertainty in Conditional Language Generation**
*Yijun Xiao, William Yang Wang*[[paper]](https://arxiv.org/abs/2103.15025) 2021.3
+ **Faithful Low-Resource Data-to-Text Generation through Cycle Training**
*Zhuoer Wang, Marcus Collins, Nikhita Vedula, Simone Filice, Shervin Malmasi, Oleg Rokhlenko*[[paper]](https://aclanthology.org/2023.acl-long.160/) 2023.7
+ `Summarization`:
+ **On FaitExcerpt of 57,063 characters
Read on GitHub
Hill Zhang · Bytedance · China
50
4
3
Erwan Le Merrer · Inria
2
1
1
1
ZhangShaolei
1
1
Qiao Jin · National Institutes of Health · United States
1
Liang
1
Sina Semnani · @Liquid4All @stanfordnlp @stanford-oval · United States
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:83ecc085b8c65f00, topic:awesome-list, desc:reading list
matched fp:83ecc085b8c65f00, topic:large-language-models