Papers by Fengyu Lu
Prophecy Distillation for Boosting Abstractive Summarization (2024.lrec-main)
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| Challenge: | Abstractive summarization models with maximum likelihood estimation generate unfaithful facts alongside ambiguous focus. |
| Approach: | They propose a framework which learns a regular summarization model to mimic the behavior of being guided by prophecy for boosting abstractive summaries. |
| Outcome: | The proposed model achieves new or matched state-of-the-art on four well-known datasets. |
Alleviating Exposure Bias in Abstractive Summarization via Sequentially Generating and Revising (2024.lrec-main)
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| Challenge: | Existing approaches to abstractive summarization suffer from exposure bias . Existing solutions bridge this gap through un- or semi-supervised holistic learning . |
| Approach: | They propose to reformat abstractive summarization to sequential generation and revision (SeGRe) this allows the model to assess the flawed summary from a global perspective and modify inappropriate expressions. |
| Outcome: | The proposed model can assess the flawed summary from a global view and modify inappropriate expressions. |
Verifiable LLM-Generated Text Detection via Projected Semantic-Structural Distributions (2026.acl-long)
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Ruochong Xiong, Qien Li, Wangwang Lian, Yulong Wan, Hanlin Xue, Zhouxing Tan, Han Yang, Fengyu Lu, Junfei Liu
| Challenge: | Existing methods for detecting LLM-Generated text suffer from distribution misalignment and limited interpretability. |
| Approach: | They propose a statistical framework utilizing supervised subspace learning to extract compact features and construct conditional semantic distributions based on syntactic structures. |
| Outcome: | The proposed framework is superior in cross-domain, cross-model, and adversarial scenarios. |