---
title: "Who cites The AI Review Lottery: — 17 citing papers"
description: "17 papers of the Elamur Research library cite «The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and…"
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---
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# Who cites «The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Rates»

17 papers of our library cite it (of the 149,439 whose bibliographies we read) · 9 with a page here, the most cited first · [← back to «The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Rates»](https://md.research.elamur.ai/free/en/papers/latona-2024-ai-review-lottery-widespread/)

1. **[DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process](https://md.research.elamur.ai/free/en/papers/zhu-2025-deepreview-improving-llm-based/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minjun Zhu, Yixuan Weng, Linyi Yang, et al.*2025 · 52 citations*
2. [**Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review arXiv:2412.01708**Rui Ye, Xianghe Pang, Jingyi Chai, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/ye-2024-there-yet-revealing-risks/)
3. [**AI-Driven Review Systems: Evaluating LLMs in Scalable and Bias-Aware Academic Reviews arXiv:2408.10365**Keith Tyser, Ben Segev, Gaston Longhitano, et al.*2024 · 34 citations*](https://md.research.elamur.ai/free/en/papers/tyser-2024-ai-driven-review-systems/)
4. **[ResearchCodeBench: Benchmarking LLMs on Implementing Novel Machine Learning Research Code](https://md.research.elamur.ai/free/en/papers/hua-2025-researchcodebench-benchmarking-llms-implementing/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Tianyu Hua; Harper Hua; Violet Xiang; Benjamin Klieger; Sang T. Truong; Weixin Liang; Fan-Yun Sun;, Nick Haber*2025 · 16 citations*
5. **[Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews](https://md.research.elamur.ai/free/en/papers/shin-2025-mind-blind-spots-focus-level/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hyungyu Shin, Jingyu Tang, Yoonjoo Lee, et al.*2025 · 14 citations*
6. [**Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment arXiv:2411.03417**Alexander Goldberg, Ihsan Ullah, Thanh Gia Hieu Khuong, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/goldberg-2024-usefulness-llms-author-checklist-assistant/)
7. **[Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews](https://md.research.elamur.ai/free/en/papers/vasu-2026-justice-judgment-unveiling-hidden-bias/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sai Suresh Macharla Vasu, Ivaxi Sheth, Hui-Po Wang, et al.*2026 · 8 citations*
8. [**AI for Auto-Research: Roadmap & User Guide arXiv:2605.18661**Lingdong Kong, Xian Sun, Wei Chow, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/kong-2026-ai-auto-research-roadmap-user/)
9. **[From Isolated Scoring to Collaborative Ranking: A Comparison-Native Framework for LLM-Based Paper Evaluation](https://md.research.elamur.ai/free/en/papers/zheng-2026-isolated-scoring-collaborative-ranking-comparison/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Pujun Zheng, Jiacheng Yao, Jinquan Zheng, et al.*2026*

8 more papers of our library cite it; they have no page here yet.

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