Papers by Weihao Sun
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion (2023.acl-long)
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Rui Li, Xu Chen, Chaozhuo Li, Yanming Shen, Jianan Zhao, Yujing Wang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Xing Xie
| Challenge: | Existing methods for embedding knowledge graphs implicitly memorize relation rules to infer missing links, but they are difficult to memorize due to the inherent deficiencies of such implicit memorization strategy. |
| Approach: | They propose a vertical learning paradigm that allows to explicitly copy target information from related factual triples for more accurate prediction. |
| Outcome: | The proposed model improves generalization ability and makes distant link prediction significantly easier. |
Using Subtext to Enhance Generative IDRR (2025.acl-short)
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| Challenge: | Arguments contain subtexts, but they are connotative and need prompts to be recognized . a lightweight subtext generator is helpful when the prompt doesn't raise a complex CoT. |
| Approach: | They leverage LLaMA to generate subtexts for argument pairs and verify their effectiveness . they construct a baseline IDRR using the decoder-only backbone LLama . |
| Outcome: | The proposed approach achieves higher F1 scores on two benchmarks than previous models. |
DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)
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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability. |
| Approach: | They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. |
| Outcome: | The proposed model outperforms baselines on three real-world datasets. |
Alleviating Performance Degradation Caused by Out-of-Distribution Issues in Embedding-Based Retrieval (2025.findings-emnlp)
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Haotong Bao, Jianjin Zhang, Qi Chen, Weihao Han, Zhengxin Zeng, Ruiheng Chang, Mingzheng Li, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Recent studies reveal query out-of-distribution issues degrading ANN performance . a distribution regularizer is introduced into the encoder training objective to encourage alignment between query and base embeddings. |
| Approach: | They introduce a distribution regularizer into the encoder training objective to encourage alignment between query and base embeddings. |
| Outcome: | The proposed method consistently improves retrieval performance across multiple datasets. |
CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models (2025.naacl-long)
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Ying Nie, Binwei Yan, Tianyu Guo, Hao Liu, Haoyu Wang, Wei He, Binfan Zheng, Weihao Wang, Qiang Li, Weijian Sun, Yunhe Wang, Dacheng Tao
| Challenge: | Large language models (LLMs) have achieved remarkable performance on various NLP tasks, yet their potential in more challenging task like finance, has not been fully explored. |
| Approach: | They propose a benchmark to assess the financial knowledge of large language models (LLMs) in China. |
| Outcome: | The proposed benchmark is the most comprehensive evaluation benchmark to date for LLMs in finance. |