Papers by Gang Lee
Enhancing Large Language Model Based Sequential Recommender Systems with Pseudo Labels Reconstruction (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have been utilized in various studies, but their training sequences and text labels can alter their pre-trained weights, reducing their ability to construct and comprehend natural language sentences. |
| Approach: | They propose a reconstruction-based LLM recommendation model that harnesses the feature extraction capability of LLMs while preserving LLM’s sentence generation abilities. |
| Outcome: | The proposed model exploits the key features of both user and item pseudo-labels generated from user reviews while training on sequential data. |
MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution (2026.acl-long)
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Jianwen Chen, Xinyu Yang, Peng Xia, Arian Azarang, Yueh Z Lee, Gang Li, Hongtu Zhu, Yun Li, Beidi Chen, Huaxiu Yao
| Challenge: | Recent advances in large reasoning models have broadened the capabilities of medical artificial intelligence. |
| Approach: | They propose a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph process based on Petri Net theory. |
| Outcome: | The proposed reasoning framework improves strong general-purpose LLMs by up to 8.9%. |
Learning to Infer Entities, Properties and their Relations from Clinical Conversations (D19-1)
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| Challenge: | Existing relation extraction models restrict inferring relations between tokens within a few neighboring sentences to avoid high computational complexity. |
| Approach: | They propose a Span Attribute Tagging (SAT) model to infer clinical entities and their properties using a hierarchical two-stage approach. |
| Outcome: | The proposed model outperforms baseline models in identifying relations between symptoms and properties by about 32% and 50% on medications and their properties. |