Work

A Knowledge-Enhanced Multi-Task Learning Model for Domain-Specific Question Answering

Year: 2025

Type: conference-paper

Abstract: This paper presents GLM-6B-QA, a question-answering system designed to improve the performance of tasks in specialized domains. GLM-6B-QA uses the ChatGLM-6B model and integrates dynamic document attention (DDAM), query refinement layer (QRL), and knowledge injection layer (KIL) within a multi-task learning (MTL) framework. This setup enhances the model's ability to understand and process complex documents, queries, and terminology. By adjusting attention dynamically, refining query representations, and incorporating external knowledge, the system improves question answering, document comprehension, and term extraction. This method addresses challenges in natural language processing tasks and provides a better approach for developing adaptable systems in specialized applications.

Source: International Conference on Information Science, Electrical and Automation Engineering (ISEAE)

DOI: 10.1109/iseae64934.2025.11041797

Authors: Gaozhe Jiang, You Yao, Huailing Mu, Qinyan Shen, Shicheng Zhou

Institutions: National University of Singapore, University of Southern California, University of California, Los Angeles, University of South Carolina, University of Minnesota

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4411584454

FWCI:

Cites: 11

Cited by:

Related to: 10