most citedAre the confidence scores of reviewers consistent with the review content? Evidence from top conference proceedings in AI

12 citations · 22 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DL2026

Measuring the Novelty of Biomedical Papers Using the Latent Distances between Knowledge Units

Yi Zhao, Heng Zhang, Yuzhuo Wang +3

Measuring the novelty of scientific papers is a central concern in research evaluation and scientometrics. From a recombination perspective, prior studies have largely focused on t…

cs.CL2026

Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI

Wenqing Wu, Chengzhi Zhang, Yi Zhao +1

With the rapid advancement of Large Language Models (LLMs), the academic community has faced unprecedented disruptions, particularly in the realm of academic communication. The pri…

cs.CL2026

NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment

Wenqing Wu, Yi Zhao, Yuzhuo Wang +4

Novelty is a core requirement in academic publishing and a central focus of peer review, yet the growing volume of submissions has placed increasing pressure on human reviewers. Wh…

cs.CL202510 cited

SC4ANM: Identifying Optimal Section Combinations for Automated Novelty Prediction in Academic Papers

Wenqing Wu, Chengzhi Zhang, Tong Bao +1

Novelty is a core component of academic papers, and there are multiple perspectives on the assessment of novelty. Existing methods often focus on word or entity combinations, which…

cs.CL202512 cited

Are the confidence scores of reviewers consistent with the review content? Evidence from top conference proceedings in AI

Wenqing Wu, Haixu Xi, Chengzhi Zhang

Peer review is vital in academia for evaluating research quality. Top AI conferences use reviewer confidence scores to ensure review reliability, but existing studies lack fine-gra…