---
title: "Fine-grained Hallucination Detection and Editing for Language Models — paper by Mishra et al. (2024)"
description: "From this they define a new task: finding hallucinated spans in generated text, labelling each span's type, and… 37 references, cited by 86 papers here."
authors: ["Abhika Mishra", "Akari Asai", "Vidhisha Balachandran", "Yizhong Wang", "Graham Neubig", "Yulia Tsvetkov", "Hannaneh Hajishirzi"]
year: 2024
venue: "arXiv:2401.06855"
source: "https://arxiv.org/abs/2401.06855"
doi: "10.48550/arxiv.2401.06855"
arxiv: "2401.06855"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mishra-2024-fine-grained-hallucination-detection-editing/"
---
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# Fine-grained Hallucination Detection and Editing for Language Models

**Authors:** Abhika Mishra, [Akari Asai](https://md.research.elamur.ai/free/en/authors/akari-asai/), [Vidhisha Balachandran](https://md.research.elamur.ai/free/en/authors/vidhisha-balachandran/), [Yizhong Wang](https://md.research.elamur.ai/free/en/authors/yizhong-wang/), [Graham Neubig](https://md.research.elamur.ai/free/en/authors/graham-neubig/), [Yulia Tsvetkov](https://md.research.elamur.ai/free/en/authors/yulia-tsvetkov/) and [Hannaneh Hajishirzi](https://md.research.elamur.ai/free/en/authors/hannaneh-hajishirzi/) · **Year:** 2024 · **Venue:** arXiv:2401.06855 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2401.06855) · [PDF](https://arxiv.org/pdf/2401.06855) · [DOI 10.48550/arxiv.2401.06855](https://doi.org/10.48550/arxiv.2401.06855) · [arXiv 2401.06855](https://arxiv.org/abs/2401.06855) · [OpenAlex](https://openalex.org/W4391013695)

**Citations:** 10 citations (OpenAlex, 2026-10-14) · [86](https://md.research.elamur.ai/free/en/papers/mishra-2024-fine-grained-hallucination-detection-editing/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Language models frequently state false facts. Most existing checkers give only a yes/no verdict for an entire response, so every error is treated the same way. The authors argue that factual mistakes come in distinct kinds, each requiring a different amount of verification effort, and they organize these kinds into a taxonomy. From this they define a new task: finding hallucinated spans in generated text, labelling each span's type, and suggesting corrections. For evaluation they build FAVABENCH, which contains about a thousand fine-grained human judgments of outputs from three LMs on information-seeking prompts across several domains. The annotations show that ChatGPT and Llama2-Chat (7B and 70B) make a variety of errors in most of their answers. They then train FAVA, a retrieval-augmented model that takes an LM output together with reference documents, flags errors and edits them. FAVA learns from synthetic data built specifically for this task. It beats ChatGPT and GPT-4, both given retrieval, at fine-grained detection. It also exceeds popular binary detectors, and its edits make texts more factual.

## Key points
- New task: automatic fine-grained hallucination detection, where errors are localized, categorized by a taxonomy and paired with proposed edits rather than receiving a single binary label.
- FAVABENCH is a benchmark of roughly 1,000 fine-grained human annotations covering outputs of three LMs across multiple domains.
- In information-seeking settings, ChatGPT, Llama2-Chat 70B and Llama2-Chat 7B show diverse hallucination types in the majority of their responses.
- FAVA is a retrieval-augmented LM trained on carefully generated synthetic data. It outperforms retrieval-augmented ChatGPT and GPT-4 on fine-grained detection in both automatic and human evaluation.
- FAVA also surpasses widely used binary hallucination detectors, and its editing improves the factuality of LM outputs.

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

## Institutions

- [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) — University, Pittsburgh, United States · 365 works · 158,048 citations
- [University of Washington](https://md.research.elamur.ai/free/en/universities/university-of-washington/) — University, Seattle, United States · 263 works · 180,581 citations
- [Allen Institute for Artificial Intelligence](https://md.research.elamur.ai/free/en/labs/allen-institute-for-artificial-intelligence/) — Research institute, Seattle, United States · 219 works · 89,712 citations

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## Cite

Cite as: Abhika Mishra, Akari Asai, Vidhisha Balachandran, Yizhong Wang, Graham Neubig, Yulia Tsvetkov and Hannaneh Hajishirzi. 2024. Fine-grained Hallucination Detection and Editing for Language Models. arXiv:2401.06855.

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

```bibtex
@misc{mishra2024fine,
  title = {{Fine-grained Hallucination Detection and Editing for Language Models}},
  author = {Abhika Mishra, Akari Asai, Vidhisha Balachandran, Yizhong Wang, Graham Neubig, Yulia Tsvetkov and Hannaneh Hajishirzi},
  year = {2024},
  doi = {10.48550/arxiv.2401.06855},
  eprint = {2401.06855},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2401.06855}
}
```

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## References

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*Updated 24 October 2026*