Papers by Mengfei Lan
Multi-label Sequential Sentence Classification via Large Language Model (2024.findings-emnlp)
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| Challenge: | Existing approaches to sequential sentence classification are constrained by model size, sequence length, and single-label setting. |
| Approach: | They propose a large language model-based framework for both single- and multi-label SSC tasks that generate SSC labels through designed prompts. |
| Outcome: | The proposed framework enhances task understanding by incorporating demonstrations and a query to describe the prediction target. |
Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning (2023.acl-long)
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| Challenge: | Existing methods for event detection often fail to detect unseen or rare events due to the lack of domain knowledge. |
| Approach: | They propose a meta learning-based framework for zero-shot event detection that uses a prompt-based prompt and a trigger-aware soft verbalizer to efficiently project output to unseen tasks. |
| Outcome: | The proposed framework performs state-of-the-art in zero-shot and few-shot scenarios on benchmark datasets FewEvent and MAVEN. |
BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels (2026.acl-short)
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| Challenge: | Existing biomedical generative retrievers lack domain semantics and hierarchical relationships among biomedically related texts. |
| Approach: | They propose a biomedical retrieval model with hierarchical multi-label contrastive learning that leverages hierarchic MeSH annotations to provide structured supervision for multi-labor contrastive training. |
| Outcome: | The proposed models achieve promising performance on biomedical retrieval, sentence similarity, and question answering tasks while remaining computationally efficient for deployment. |