---
title: "HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models — Li 2023"
description: "Large language models such as ChatGPT often produce content that contradicts its source or cannot be checked… 35 references, cited by 325 papers here."
authors: ["Junyi Li", "Xiaoxue Cheng", "Wayne Xin Zhao", "Jian-Yun Nie", "Ji-Rong Wen"]
year: 2023
venue: "arXiv:2305.11747"
source: "https://arxiv.org/abs/2305.11747"
doi: "10.48550/arxiv.2305.11747"
arxiv: "2305.11747"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/li-2023-halueval-large-scale-hallucination-evaluation/"
---
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# HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models

**Authors:** [Junyi Li](https://md.research.elamur.ai/free/en/authors/junyi-li/), [Xiaoxue Cheng](https://md.research.elamur.ai/free/en/authors/xiaoxue-cheng/), [Wayne Xin Zhao](https://md.research.elamur.ai/free/en/authors/wayne-xin-zhao/), [Jian-Yun Nie](https://md.research.elamur.ai/free/en/authors/jian-yun-nie/) and [Ji-Rong Wen](https://md.research.elamur.ai/free/en/authors/ji-rong-wen/) · **Year:** 2023 · **Venue:** arXiv:2305.11747 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2305.11747) · [PDF](https://arxiv.org/pdf/2305.11747) · [DOI 10.48550/arxiv.2305.11747](https://doi.org/10.48550/arxiv.2305.11747) · [arXiv 2305.11747](https://arxiv.org/abs/2305.11747) · [OpenAlex](https://openalex.org/W4389518784)

**Citations:** 321 citations (OpenAlex, 2026-10-14) · [325](https://md.research.elamur.ai/free/en/papers/li-2023-halueval-large-scale-hallucination-evaluation/cited-by/) in our library, of the 140,884 papers whose bibliographies we read

## Summary
Large language models such as ChatGPT often produce content that contradicts its source or cannot be checked against known facts. HaluEval is a benchmark that measures how well LLMs detect these hallucinations and shows which kinds of content they tend to make up. It holds 35,000 examples. 5,000 are Alpaca user queries paired with ChatGPT answers. These queries were picked because three sampled answers to each disagreed most with one another. Human annotators then judged each answer for hallucination and marked the faulty spans. The other 30,000 examples cover question answering, knowledge-grounded dialogue and summarization. For each one, ChatGPT wrote a convincing but wrong counterpart in two stages. First it sampled candidates using one-pass and conversational instructions built around task-specific hallucination patterns. Then a filtering prompt with ground-truth examples picked the hardest candidate. Annotators found hallucinations in about 19.5% of ChatGPT's answers. Current LLMs, ChatGPT included, struggle to spot hallucinations, and supplying retrieved knowledge helps more than chain-of-thought prompting. This gives hallucination research a shared, large-scale yardstick.

## Key points
- **Composition:** 35,000 samples. 5,000 are human-annotated ChatGPT responses to Alpaca instructions. The other 30,000 are generated hallucinated counterparts of instances drawn from HotpotQA, OpenDialKG and CNN/Daily Mail. The data is available at github.com/RUCAIBox/HaluEval.
- **Generation method:** a "sampling-then-filtering" pipeline run entirely with ChatGPT. Each instruction combines an intention description, a hallucination pattern and few-shot demonstrations. There are 4 patterns for QA (comprehension, factualness, specificity, inference), 3 for dialogue (extrinsic-soft, extrinsic-hard, extrinsic-grouped) and 3 for summarization (factual, non-factual, intrinsic).
- **ChatGPT as a detector:** accuracy is 62.59% on QA, 72.40% on dialogue, 58.53% on summarization and 86.22% on general queries. That is weak, given that ChatGPT generated many of these samples itself.
- **Improvement strategies:** adding external knowledge lifts QA accuracy to 76.83% and general-query accuracy to 90.73%. Chain-of-thought gives mixed results: it helps summarization (61.21%) but slightly hurts QA (59.58%). Showing ground-truth answers next to hallucinated ones confuses the model, dropping QA accuracy to 49.19%.
- **Limitations:** sample quality depends on how well ChatGPT follows the complex sampling instructions. The benchmark tests whether models can recognise hallucinations but doesn't study why they occur.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

Topics: [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · [📏 Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/)

## Institutions

- [Université de Montréal](https://md.research.elamur.ai/free/en/universities/universite-de-montreal/) — University, Montreal, Canada · 42 works · 71,559 citations
- [Renmin University of China](https://md.research.elamur.ai/free/en/universities/renmin-university-of-china/) — University, Beijing, China · 26 works · 13,818 citations

## Cite

Cite as: Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie and Ji-Rong Wen. 2023. HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models. arXiv:2305.11747.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{li2023halueval,
  title = {{HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models}},
  author = {Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie and Ji-Rong Wen},
  year = {2023},
  doi = {10.48550/arxiv.2305.11747},
  eprint = {2305.11747},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2305.11747}
}
```

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