# Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review

arXiv: https://arxiv.org/abs/2412.01708 · HTML: https://mlsanity.com/paper/2412.01708/

## Key facts

- Cited by 49 papers in the map (data through 7 Oct 2026).
- Builds on 13 of its 53 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Large Language Models are not Fair Evaluators](https://mlsanity.com/paper/2305.17926/) (2023)
- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://mlsanity.com/paper/2404.04475/) (2024)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs](https://mlsanity.com/paper/2401.06373/) (2024)
- [ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing](https://mlsanity.com/paper/2306.00622/) (2023)
- [Style Over Substance: Evaluation Biases for Large Language Models](https://mlsanity.com/paper/2307.03025/) (2023)
- [The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Rates](https://mlsanity.com/paper/2405.02150/) (2024)
- [AI-Driven Review Systems: Evaluating LLMs in Scalable and Bias-Aware Academic Reviews](https://mlsanity.com/paper/2408.10365/) (2024)
- [Is Your Paper Being Reviewed by an LLM? Investigating AI Text Detectability in Peer Review](https://mlsanity.com/paper/2410.03019/) (2024)

## Built on by (most cited first, 30 of 49)

- [DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process](https://mlsanity.com/paper/2503.08569/) (2025)
- [OpenReviewer: A Specialized Large Language Model for Generating Critical Scientific Paper Reviews](https://mlsanity.com/paper/2412.11948/) (2024)
- [Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety](https://mlsanity.com/paper/2502.05206/) (2025)
- [Large language models for automated scholarly paper review: A survey](https://mlsanity.com/paper/2501.10326/) (2025)
- [Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards](https://mlsanity.com/paper/2505.04966/) (2025)
- [Evaluating Sakana's AI Scientist: Bold Claims, Mixed Results, and a Promising Future?](https://mlsanity.com/paper/2502.14297/) (2025)
- [Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews](https://mlsanity.com/paper/2502.17086/) (2025)
- [Is Your Paper Being Reviewed by an LLM? Benchmarking AI Text Detection in Peer Review](https://mlsanity.com/paper/2502.19614/) (2025)
- [How Far Are AI Scientists from Changing the World?](https://mlsanity.com/paper/2507.23276/) (2025)
- [Hidden Prompts in Manuscripts Exploit AI-Assisted Peer Review](https://mlsanity.com/paper/2507.06185/) (2025)
- [Unveiling the Merits and Defects of LLMs in Automatic Review Generation for Scientific Papers](https://mlsanity.com/paper/2509.19326/) (2025)
- [FLAWS: A Benchmark for Error Identification and Localization in Scientific Papers](https://mlsanity.com/paper/2511.21843/) (2025)
- [Stop Automating Peer Review Without Rigorous Evaluation](https://mlsanity.com/paper/2605.03202/) (2026)
- [Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews](https://mlsanity.com/paper/2509.13400/) (2025)
- [Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem](https://mlsanity.com/paper/2506.08134/) (2025)
- [DeepReviewer 2.0: A Traceable Agentic System for Auditable Scientific Peer Review](https://mlsanity.com/paper/2604.09590/) (2026)
- [Paraphrasing Adversarial Attack on LLM-as-a-Reviewer](https://mlsanity.com/paper/2601.06884/) (2026)
- [Combatting Dimensional Collapse in LLM Pre-Training Data via Diversified File Selection](https://mlsanity.com/paper/2504.20644/) (2025)
- [SafeReview: Defending LLM-based Review Systems Against Adversarial Hidden Prompts](https://mlsanity.com/paper/2604.26506/) (2026)
- [From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent](https://mlsanity.com/paper/2606.13349/) (2026)
- [Graph-Guided Passage Retrieval for Author-Centric Structured Feedback](https://mlsanity.com/paper/2505.14376/) (2025)
- [The Feasibility of Topic-Based Watermarking on Academic Peer Reviews](https://mlsanity.com/paper/2505.21636/) (2025)
- [ReviewGuard: Enhancing Deficient Peer Review Detection via LLM-Driven Data Augmentation](https://mlsanity.com/paper/2510.16549/) (2025)
- ["Give a Positive Review Only": An Early Investigation Into In-Paper Prompt Injection Attacks and Defenses for AI Reviewers](https://mlsanity.com/paper/2511.01287/) (2025)
- [ReviewGrounder: Improving Review Substantiveness with Rubric-Guided, Tool-Integrated Agents](https://mlsanity.com/paper/2604.14261/) (2026)
- [PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality](https://mlsanity.com/paper/2606.20897/) (2026)
- [SEAGraph: Unveiling the Whole Story of Paper Review Comments](https://mlsanity.com/paper/2412.11939/) (2024)
- [Multi-Agent LLMs for Generating Research Limitations](https://mlsanity.com/paper/2601.11578/) (2025)
- [Do LLMs Favor LLMs? Quantifying Interaction Effects in Peer Review](https://mlsanity.com/paper/2601.20920/) (2026)
- [When AI reviews science: Can we trust the referee?](https://mlsanity.com/paper/2604.23593/) (2026)

## Abstract (arXiv)

Scholarly peer review is a cornerstone of scientific advancement, but the system is under strain due to increasing manuscript submissions and the labor-intensive nature of the process. Recent advancements in large language models (LLMs) have led to their integration into peer review, with promising results such as substantial overlaps between LLM- and human-generated reviews. However, the unchecked adoption of LLMs poses significant risks to the integrity of the peer review system. In this study, we comprehensively analyze the vulnerabilities of LLM-generated reviews by focusing on manipulation and inherent flaws. Our experiments show that injecting covert deliberate content into manuscripts allows authors to explicitly manipulate LLM reviews, leading to inflated ratings and reduced alignment with human reviews. In a simulation, we find that manipulating 5% of the reviews could potentially cause 12% of the papers to lose their position in the top 30% rankings. Implicit manipulation, where authors strategically highlight minor limitations in their papers, further demonstrates LLMs' susceptibility compared to human reviewers, with a 4.5 times higher consistency with disclosed limitations. Additionally, LLMs exhibit inherent flaws, such as potentially assigning higher ratings to incomplete papers compared to full papers and favoring well-known authors in single-blind review process. These findings highlight the risks of over-reliance on LLMs in peer review, underscoring that we are not yet ready for widespread adoption and emphasizing the need for robust safeguards.