Papers by Wen Yujia
Enhancing Generative Retrieval with Reinforcement Learning from Relevance Feedback (2023.emnlp-main)
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| Challenge: | End-to-end generative retrieval models produce document identifiers in response to a query . however, this approach has two challenges: an overemphasis on top-1 results at the expense of overall ranking quality. |
| Approach: | They propose a generative retrieval model with reinforcement learning from relevance feedback to align token-level docid generation with document-level relevance estimation. |
| Outcome: | The proposed model aligns token-level docid generation with document-level relevance estimation. |
Incomplete In-context Learning (2026.acl-long)
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Wenqiang Wang, Wen Yujia, Yan Xiao, Zhifeng Chen, Yangshijie Zhang, Peng Chen, Mingbo Yang, Xiaochun Cao
| Challenge: | Existing in-context learning assumes the retrieval dataset contains demonstrations for all output label spaces. |
| Approach: | They propose a framework with train-free and train-based variants to address IICL . they propose to integrate a dataset with labeled demonstrations for each output space . |
| Outcome: | The proposed framework outperforms existing methods under incomplete retrieval datasets and even outperformed ICL with complete labels. |
Large Language Model-based Human-Agent Collaboration for Complex Task Solving (2024.findings-emnlp)
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| Challenge: | Recent advances in large language models have led to the development of LLM-based autonomous agents. |
| Approach: | They propose a Reinforcement Learning-based Human-Agent Collaboration method which trains a policy model to determine the most opportune stages for human intervention within the task-solving process. |
| Outcome: | The proposed method improves human-agent collaboration significantly through well-planned, limited human intervention. |
On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)
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Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou
| Challenge: | Pre-trained language models (PLMs) can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but require much more training time than fine-timing. |
| Approach: | They empirically investigate the transferability of soft prompts across different downstream tasks and PLMs to determine what decides prompt transferability. |
| Outcome: | The proposed method can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but requires much more training time than fine-timing. |
Personalized Abstractive Summarization by Tri-agent Generation Pipeline (2024.findings-eacl)
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| Challenge: | Existing research shows that large language models do not consistently satisfy users' preferences or expectations. |
| Approach: | They propose a tri-agent generation pipeline that includes a generator, an instructor, and an editor to enhance output personalization. |
| Outcome: | The proposed pipeline generates outputs that better meet user expectations on two abstractive summarization datasets. |