Papers by Zihan Qin
CreativeBench: Benchmarking and Enhancing Machine Creativity via Self-Evolving Challenges (2026.findings-acl)
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| Challenge: | Increasing saturation of web data limits further scaling of model intelligence. |
| Approach: | They propose a benchmark to evaluate machine creativity in code generation that combines combinatorial and exploratory creativity through reverse engineering and self-play. |
| Outcome: | The proposed benchmark targets combinatorial and exploratory creativity through reverse engineering and self-play. |
Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer (2021.emnlp-main)
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| Challenge: | Comparative Preference Classification (CPC) is a natural language processing task that predicts whether a preference comparison exists between two entities in a given sentence . |
| Approach: | They propose a sentiment analyzer that learns sentiments to individual entities via domain adaptive knowledge transfer. |
| Outcome: | Experiments on the CompSent-19 dataset present a significant improvement on the F1 scores over the best existing CPC approaches. |
Sinkhorn Distance Minimization for Knowledge Distillation (2024.lrec-main)
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Xiao Cui, Yulei Qin, Yuting Gao, Enwei Zhang, Zihan Xu, Tong Wu, Ke Li, Xing Sun, Wengang Zhou, Houqiang Li
| Challenge: | Existing knowledge distillation methods investigate divergence measures but fail to deliver effective supervision when few distribution overlap exists between teacher and student. |
| Approach: | They propose a knowledge distillation method that exploits the Sinkhorn distance to ensure a nuanced assessment of the disparity between teacher and student distributions. |
| Outcome: | The proposed method outperforms state-of-the-art methods on all kinds of LLMs with encoder-only, encoder decoder, and decoded architectures. |
WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)
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Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Long-form question answering requires two procedures: information retrieval and information synthesis. |
| Approach: | They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time . |
| Outcome: | The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset . |
A Semantic Search Engine for Mathlib4 (2024.findings-emnlp)
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| Challenge: | Lean is an interactive theorem prover that enables verification of formal proofs . however, searching for theoretical proofs in mathlib4 can be challenging for beginners . we present a semantic search engine that accepts informal queries and finds theorels . |
| Approach: | They propose a semantic search engine for theorems in mathlib4 that accepts informal queries and finds relevant theorels. |
| Outcome: | The proposed search engine accepts informal queries and finds theorems . it compares with other search engines that struggle to find theoretical proofs based on informal queries . |
MessToClean: Evidence-Grounded Structure-Preserving Reconstruction for Real-World Degraded Exam Paper Images (2026.acl-long)
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Jiayi Tuo, Cheng Tang, Zihan Wang, Chenyue Zhou, Yao Li, Yanbiao Ma, Chao Wang, Wei Dai, Mingxuan Wang, Shitong Qin, Ziwei Zhao
| Challenge: | Existing Multimodal Large Language Models (MLLMs) fail under RDEI, leading to disrupted structure and evidence-unsupported hallucinations. |
| Approach: | They propose a backbone-agnostic, evidence-driven pipeline that treats off-the-shelf MLLMs as interchangeable components to improve stem consistency and figure consistency. |
| Outcome: | The proposed pipeline improves stem consistency by 1.01-3.18%, figure consistency by 0.50-49.16%, and refusal F1 by 1.06-10.88% across question types. |
Decompose, Prioritize, and Eliminate: Dynamically Integrating Diverse Representations for Multimodal Named Entity Recognition (2024.lrec-main)
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| Challenge: | Existing research on multi-modal Named Entity Recognition (MNER) does not integrate all multi-modal representations to provide rich contextual information to improve NER. |
| Approach: | They propose an iterative reasoning framework that integrates all the diverse multi-modal representations following the strategy of "decompose, prioritize, and eliminate" . they propose to use hierarchically connected fusion layers to prioritize transitions from "easy-to-hard" and "coarse-to fine" |
| Outcome: | The proposed framework integrates all the diverse multi-modal representations following the strategy of "decompose, prioritize, and eliminate". |