Papers by Wenwen Ouyang
FT-MDT: Extracting Decision Trees from Medical Texts via a Novel Low-rank Adaptation Method (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Existing methods for extracting medical decision trees rely on manual annotation . PI-LoRA is a low-rank adaptation method for extract medical decision tree from clinical guidelines and textbooks . |
| Approach: | They propose a low-rank adaptation method for automatically extracting medical decision trees from clinical guidelines and textbooks. |
| Outcome: | The proposed method outperforms existing methods for the Text2MDT task while maintaining a lightweight architecture. |
AMAS: Adaptively Determining Communication Topology for LLM-based Multi-agent System (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Large language models (LLMs) have revolutionized natural language processing, but their practical implementation as autonomous multi-agent systems remains fraught with unresolved challenges. |
| Approach: | They propose a dynamic graph selector that redefines LLM-based MAS by exploiting the intrinsic properties of individual inputs to intelligently direct query trajectories. |
| Outcome: | The proposed framework exceeds state-of-the-art approaches in question answering, mathematical deduction, and code generation benchmarks. |
Time-LlaMA: Adapting Large Language Models for Time Series Modeling via Dynamic Low-rank Adaptation (2025.acl-srw)
Copied to clipboard
| Challenge: | Recent studies have demonstrated that large language models possess robust pattern recognition and semantic understanding capabilities over time series data. |
| Approach: | They propose a time series model that converts time series input into token embeddings and aligns time sequence embeddables with text prompts. |
| Outcome: | The proposed framework achieves the state-of-the-art (SOTA) performance and has potentials for wide industrial usages. |