---
title: "Is Your Paper Being Reviewed by an LLM? Investigating AI Text"
description: "Growing submission volumes at AI conferences may tempt overloaded reviewers to quietly hand their… 32 references, cited by 10 papers here."
authors: ["Sungduk Yu", "Man Luo", "Avinash Madasu", "Vasudev Lal", "Phillip Howard"]
year: 2024
venue: "arXiv:2410.03019"
source: "https://arxiv.org/abs/2410.03019"
doi: "10.48550/arxiv.2410.03019"
arxiv: "2410.03019"
licence: "arXiv non-exclusive distribution license"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yu-2024-paper-being-reviewed-llm/"
---
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# Is Your Paper Being Reviewed by an LLM? Investigating AI Text Detectability in Peer Review

**Authors:** [Sungduk Yu](https://md.research.elamur.ai/free/en/authors/sungduk-yu/), [Man Luo](https://md.research.elamur.ai/free/en/authors/man-luo/), Avinash Madasu, [Vasudev Lal](https://md.research.elamur.ai/free/en/authors/vasudev-lal/) and Phillip Howard · **Year:** 2024 · **Venue:** arXiv:2410.03019 · **Licence:** [arXiv non-exclusive distribution license](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2410.03019) · [PDF](https://arxiv.org/pdf/2410.03019) · [DOI 10.48550/arxiv.2410.03019](https://doi.org/10.48550/arxiv.2410.03019) · [arXiv 2410.03019](https://arxiv.org/abs/2410.03019) · [OpenAlex](https://openalex.org/W4403885547)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [10](https://md.research.elamur.ai/free/en/papers/yu-2024-paper-being-reviewed-llm/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Growing submission volumes at AI conferences may tempt overloaded reviewers to quietly hand their work to LLMs, which undermines trust in peer review. This paper asks whether such reviews can be caught one at a time, rather than only estimated in aggregate. The authors pulled ICLR papers and human reviews through the OpenReview API. They then produced 16,000 synthetic reviews with GPT-4o and Llama-3.1 (70b) for 500 papers per year (2021–2024), varying the prompt across four reviewer personas and supplying ICLR reviewer guidelines. They benchmarked fine-tuned RoBERTa and Longformer detectors, the commercial Originality AI API, and LLM-as-a-judge setups. They also introduced an "anchor" method: an LLM writes its own review of the same paper, and a test review is flagged when its embedding is highly cosine-similar to that anchor. Existing tools missed many GPT-4o reviews unless they tolerated many false alarms. The anchor approach caught most GPT-4o reviews at a 5% false positive rate. This matters because conferences need detectors that rarely accuse honest reviewers.

## Key points
- Data: ICLR 2019–2024 submissions and reviews via the OpenReview API. AI reviews were generated by GPT-4o and Llama-3.1 (70b) using prompts adapted from Lu et al., with four personas: balanced, nitpicky, innovative and conservative.
- On ICLR 2022 at a false positive rate of 0.05, the GPT-4o and Llama 3.1 anchor detectors found about 97% and 95% of GPT-4o reviews, and 87–90% of Llama reviews.
- At the same false positive rate, Originality AI flagged only slightly over half of GPT-4o reviews, although it detected nearly all Llama-written ones.
- AUC on GPT-4o reviews: RoBERTa 0.71, Longformer 0.88, Originality AI 0.94, both anchor methods 0.99. On Llama reviews, Originality AI led with 0.99.
- An analysis of GPT-4o judge rationales, clustered with SFR-Embedding and t-SNE, showed it relies on style cues such as formal tone and generic critique. It sometimes cited non-native phrasing as a sign of AI text, which raises fairness concerns.

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

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## Cite

Cite as: Sungduk Yu, Man Luo, Avinash Madasu, Vasudev Lal and Phillip Howard. 2024. Is Your Paper Being Reviewed by an LLM? Investigating AI Text Detectability in Peer Review. arXiv:2410.03019.

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

```bibtex
@misc{yu2024is,
  title = {{Is Your Paper Being Reviewed by an LLM? Investigating AI Text Detectability in Peer Review}},
  author = {Sungduk Yu, Man Luo, Avinash Madasu, Vasudev Lal and Phillip Howard},
  year = {2024},
  doi = {10.48550/arxiv.2410.03019},
  eprint = {2410.03019},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2410.03019}
}
```

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## References

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*Updated 3 November 2026*