Kunpeng Zhang


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Evaluating Reading Comprehension Exercises Generated by LLMs: A Showcase of ChatGPT in Education Applications
Changrong Xiao | Sean Xin Xu | Kunpeng Zhang | Yufang Wang | Lei Xia
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

The recent advancement of pre-trained Large Language Models (LLMs), such as OpenAI’s ChatGPT, has led to transformative changes across fields. For example, developing intelligent systems in the educational sector that leverage the linguistic capabilities of LLMs demonstrates a visible potential. Though researchers have recently explored how ChatGPT could possibly assist in student learning, few studies have applied these techniques to real-world classroom settings involving teachers and students. In this study, we implement a reading comprehension exercise generation system that provides high-quality and personalized reading materials for middle school English learners in China. Extensive evaluations of the generated reading passages and corresponding exercise questions, conducted both automatically and manually, demonstrate that the system-generated materials are suitable for students and even surpass the quality of existing human-written ones. By incorporating first-hand feedback and suggestions from experienced educators, this study serves as a meaningful pioneering application of ChatGPT, shedding light on the future design and implementation of LLM-based systems in the educational context.


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Interpreting Twitter User Geolocation
Ting Zhong | Tianliang Wang | Fan Zhou | Goce Trajcevski | Kunpeng Zhang | Yi Yang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Identifying user geolocation in online social networks is an essential task in many location-based applications. Existing methods rely on the similarity of text and network structure, however, they suffer from a lack of interpretability on the corresponding results, which is crucial for understanding model behavior. In this work, we adopt influence functions to interpret the behavior of GNN-based models by identifying the importance of training users when predicting the locations of the testing users. This methodology helps with providing meaningful explanations on prediction results. Furthermore, it also initiates an attempt to uncover the so-called “black-box” GNN-based models by investigating the effect of individual nodes.


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Reducing infrequent-token perplexity via variational corpora
Yusheng Xie | Pranjal Daga | Yu Cheng | Kunpeng Zhang | Ankit Agrawal | Alok Choudhary
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)


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Active Learning with Constrained Topic Model
Yi Yang | Shimei Pan | Doug Downey | Kunpeng Zhang
Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces