Papers by Kun Ni
SrDetection: A Self-Referential Framework for Data Leakage Detection in Code Large Language Models (2026.findings-acl)
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Shuaimin Li, Liyang Fan, Zeyang li, Zhuoyue Wan, Yufang Lin, Shiwen Ni, Feiteng Fang, Hamid Alinejad-Rokny, Yuanfeng Song, Kun Jing, Chen Jason Zhang, Min Yang
| Challenge: | Existing methods for evaluating code large language models assume access to proprietary training corpora or use external reference sets with manually tuned, non-generalizable thresholds. |
| Approach: | They propose a framework for self-referential leakage detection for gray-box and black-box settings. |
| Outcome: | The proposed framework improves average F1 by 21.52 points in the gray-box setting and 14.46 points in black-box settings over strong baselines. |
Learning Improvised Chatbots from Adversarial Modifications of Natural Language Feedback (2020.findings-emnlp)
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| Challenge: | Currently, user feedback contains extraneous sequences hindering their usefulness as a training sample. |
| Approach: | They propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation and fools the discriminator which distinguishes feedback from natural responses. |
| Outcome: | The proposed model improves the original chatbot performance from 69.94%to 75.96% in ranking correct responses on the PERSONACHATdataset. |