Papers by Yuchen Bian
On Attention Redundancy: A Comprehensive Study (2021.naacl-main)
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| Challenge: | Attention redundancy has been observed among attention heads but has not been deeply studied in the literature. |
| Approach: | They propose a multi-layer multi-head self-attention mechanism which is widely applied in modern neural language models. |
| Outcome: | The proposed model is useful for interpretation and model compression. |
Training on Lexical Resources (2022.lrec-1)
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| Challenge: | In this paper, we fine-tune pretrained deep nets such as BERT and ERNIE . at inference time, these nets can be used to distinguish synonyms from antonyms . |
| Approach: | They propose to use lexical resources to fine-tune pretrained deep nets such as BERT and ERNIE to distinguish synonyms from antonyms. |
| Outcome: | The proposed method can be applied to multiword expressions, out of vocabulary words, morphological variants and more. |
Data Collection vs. Knowledge Graph Completion: What is Needed to Improve Coverage? (2021.emnlp-main)
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| Challenge: | Knowledge Graph Completion (KGC) attempts to learn missing links from subsets. |
| Approach: | This survey/position paper discusses ways to improve coverage of resources such as WordNet. |
| Outcome: | The proposed method improves WordNet coverage by reducing the number of words in the sample and reducing unbalanced corpora. |
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)
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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo
| Challenge: | Existing research on reinforcement learning for LLMs under data scarcity has not been unified. |
| Approach: | They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric. |
| Outcome: | The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area. |