arXiv AI By Zhuo Chen, Hao Zeng, Jiawei Liu, Guoxiu He, Le Cai, Liu Haotan, Li Wenbo, Yong Huang, Wei Lu

Breaking the Illusion of Review Reliability under Static Evaluation: SCOPE Fuzzing for LLM-based Scientific Reviewers

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arXiv Computation and Language
Aug 24

Don't Judge Code by Its Cover: Exploring Biases in LLM Judges for Code Evaluation

Large language models (LLMs) are increasingly used as judges for code evaluation, assessing correctness without reference implementations. This study investigates whether LLM judges can fairly evaluate semantically equivalent code that differs in superficial aspects such as variable names, comments, or formatting. The authors define six types of potential bias, conduct experiments across five programming languages and multiple LLMs, and find that all tested judges exhibit both positive and negative biases, leading to inflated or unfairly low scores even when prompted to generate test cases.

By Jiwon Moon, Yerin Hwang, Dongryeol Lee, Taegwan Kang, Yongil Kim, Kyomin Jung
Hugging Face Trending Papers
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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents

Large language models (LLMs) increasingly support complex professional tasks, yet their capabilities in rule-intensive document review remain insufficiently evaluated. National standard documents, such as China GB/T standards, offer a representative testbed: they are lengthy, highly structured, and governed by explicit rules for scope, terminology, normative wording, and cross-section consistency.

arXiv AI
Aug 11

How Can Rhetoric Reward-Hack AI Reviewers? Dissecting Rhetorical Sensitivity in AI-Based Peer Review

arXiv:2608. 08975v1 Announce Type: cross Abstract: As large language models increasingly participate in scientific evaluation, we investigate a potential form of reward hacking: how rhetorical choices shape AI-review judgments when reported scientific content is preserved and how these effects vary across evaluation conditions.

By Ming Li, Chenguang Wang, Xirui Li, Xinyue Zeng, Dianqi Li, Peng Shi, Dawei Zhou, Tianyi Zhou
arXiv AI
Aug 28

From Static to Dynamic: Benchmarking Real-World Code Review with MCR-Bench

The paper introduces MCR-Bench, a benchmark for realistic multi‑round code review that includes 2,269 real‑world tasks across five programming languages, each annotated with fine‑grained defect information and dynamic state labels. Experiments with mainstream large language models show limited overall performance, especially as interaction rounds increase, and reveal that model accuracy varies by defect type and severity. Error analysis identifies key failure mechanisms such as cross‑round temporal misalignment and insufficient long‑range memory.

By Dewu Zheng, Yanlin Wang, Xiwen Wang, Kefeng Duan, Hongyu Zhang, Xilin Liu, Yuchi Ma, Zibin Zheng