# Paper Rebuttal Tips

Paper Rebuttal Tips is a Chinese-language guide for writing rebuttals to peer-review comments at AI and machine-learning conferences, organized as reviewer-question, weak-answer, recommended-answer and key-point entries.

Paper Rebuttal Tips collects common reviewer objections seen in AI and machine-learning conference submissions and, for each one, lays out the typical criticism, an easy-to-fall-into weak response, a more effective recommended response, and the underlying key point to keep in mind. The 28 numbered tips are grouped into three themes: novelty, motivation and the theoretical boundaries of a method; writing clarity, coverage of related work and handling reviewer misunderstandings or low-quality reviews; and experimental and evaluation evidence, covering baselines, ablations, overhead, generalization, statistical significance and reproducibility.

A further resource section links Chinese-language articles and a recorded lecture on rebuttal strategy, along with a short list of related tools and agents built to help draft or structure academic rebuttals. The authors note the tips reflect their own experience and are not guaranteed to fit every conference's specific rebuttal rules, word limits or restrictions on new experiments or external links.

Website: https://github.com/MLNLP-World/Paper-Rebuttal-Tips