---
title: "Hallucinated but Factual! Inspecting the — Cao 2022"
description: "Abstractive summarizers frequently produce content that the source document does not support, and prior work usually treats all such hallucinations as…"
authors: ["Meng Cao", "Yue Dong", "Jackie Cheung"]
year: 2022
venue: "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2022.acl-long.236/"
doi: "10.18653/v1/2022.acl-long.236"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/cao-2022-hallucinated-but-factual-inspecting-factuality/"
---
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# Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization

**Authors:** [Meng Cao](https://md.research.elamur.ai/free/en/authors/meng-cao/), [Yue Dong](https://md.research.elamur.ai/free/en/authors/yue-dong/), [Jackie Cheung](https://md.research.elamur.ai/free/en/authors/jackie-chi-kit-cheung/) · **Year:** 2022 · **Venue:** Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.acl-long.236/) · [PDF](https://aclanthology.org/2022.acl-long.236.pdf) · [DOI 10.18653/v1/2022.acl-long.236](https://doi.org/10.18653/v1/2022.acl-long.236) · [OpenAlex](https://openalex.org/W4206850841)

**Citations:** 75 citations (OpenAlex, 2026-10-14) · [92](https://md.research.elamur.ai/free/en/papers/cao-2022-hallucinated-but-factual-inspecting-factuality/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
Abstractive summarizers frequently produce content that the source document does not support, and prior work usually treats all such hallucinations as errors. Cao, Dong and Cheung argue that many hallucinated entities are actually true according to world knowledge and can add useful background. For example, a summary might state a person's official title even when the article omits it. They build XEnt, an entity-level annotation of BART summaries on XSum that labels entities as non-hallucinated, factual hallucinations or non-factual hallucinations. Their detector, EntFA, uses two features: an entity's prior probability from a masked language model (MLM) that sees only the summary context, and its posterior probability from a conditional masked LM (CMLM) that also sees the source. A source-overlap flag is added, and a KNN classifier labels each entity. EntFA beats five baselines on XEnt and on the Maynez et al. (2020) annotations, and it correlates strongly with human factuality judgments. Used as a reward in offline RL, it makes summaries more factual without making them more extractive.

## Key points
- About 30% of entities that BART generates on the XSum test set are hallucinated, and over half of those hallucinated entities are factual.
- Non-factual hallucinations cluster where both prior and posterior probabilities are low. Factual hallucinations tend to receive high posterior probability from CMLM.
- Ablation on the XEnt test set shows the role of each feature:
  - Full EntFA reaches F1 81.82 for factuality and 91.73 for hallucination detection.
  - Removing the posterior feature drops factuality F1 to 70.30.
  - Removing the overlap feature drops hallucination F1 to 74.83.
- In the RL setup with reward weight 2.0 on XSum, the share of entities not found in the source falls from 42.0% (MLE) to 37.2%. The share of entities judged factual rises from 82.8% to 90.1%. The system also outperforms loss truncation baselines.
- Code is released as EntFA at github.com/mcao516/EntFA.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Summarization](https://md.research.elamur.ai/free/en/topics/summarization/)

## Cite

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

```bibtex
@inproceedings{cao2022hallucinated,
  title = {{Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization}},
  author = {Cao, Meng and Dong, Yue and Cheung, Jackie},
  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2022},
  doi = {10.18653/v1/2022.acl-long.236},
  url = {https://doi.org/10.18653/v1/2022.acl-long.236}
}
```

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