| Proj No. | A1060-251 |
| Title | Leveraging Large Language Models for Event Information Extraction |
| Summary | Event Information Extraction (EIE) plays a crucial role in various applications. Traditional methods rely on rule-based systems or statistical approaches, which often struggle with generalization and adaptability. Recent advancements in Large Language Models (LLMs) present an opportunity to revolutionize EIE by leveraging deep contextual understanding and multi-modal capabilities. This project aims to develop an LLM-driven framework for robust and scalable event extraction. The key objectives include extracting structured event information and exploring multimodal integration by incorporating text, images, and other data sources. |
| Supervisor | A/P Mao Kezhi (Loc:S2 > S2 B2C > S2 B2C 84, Ext: +65 67904284) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Machine Learning and Data Analytics Lab (Loc: S2.1, B4-01) |
| Single/Group: | Single |
| Area: | Intelligent Systems and Control Engineering |
| ISP/RI/SMP/SCP?: |