Benchmarking Foundation Models with Language-Model-as-an-Examiner (2023 · 106 citations)
Open-ended question answering benchmarks face two obstacles. Test items may already appear in pre-training data, which inflates scores. Free-form answers are also hard to grade automatically because no single reference covers every acceptable response. Bai et al. propose Language-Model-as-an-Examiner, in which a…
AgentReview: Exploring Peer Review Dynamics with LLM Agents (2024 · 86 citations)
Studying peer review empirically is hard. Outcomes depend on many interacting factors, some important ones such as reviewer bias or motivation are hidden, and real review records are confidential. AgentReview addresses these problems by simulating conference reviewing entirely with LLM agents playing reviewers…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

Benchmarking Foundation Models with Language-Model-as-an-ExaminerConference on Neural Information Processing Systems (NeurIPS)Yushi Bai, Jiahao Ying, Yixin Cao, et al.2023 · 106 citations
AgentReview: Exploring Peer Review Dynamics with LLM AgentsConference on Empirical Methods in Natural Language Processing (EMNLP)Yiqiao Jin, Qinlin Zhao, Yiyang Wang, et al.2024 · 86 citations
Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards ↗arXiv (Cornell University) · no summary yetFang Wu et al.2025 · 3 citations

Co-authors here

12 of 15 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 134 times (of the 149,439 papers whose bibliographies we read); 45 citing papers with a page here →.

Updated 2 November 2026