Work

Domain Adaptation of Financial Decision Code Generation Framework Based on Contrastive Learning and Dual-Agent Collaboration

Year: 2025

Type: conference-paper

Abstract: This paper discussed the limitations of traditional financial analysis proceeds through a AI-assisted framework integrating contrastive learning and dual-agent collaboration. Proposed framework enhances accuracy and semantic alignment in financial decision-making tasks, achieving accuracy of 89% and decision quality of 0.83, with a latency of 0.76 seconds response. Experimental results show that significant improvements in financial educational effectiveness, with the experimental group demonstrating quicker task completion and better knowledge transfer rate. The proposed framework's dynamic knowledge graph engine combines real-time updates and complex query optimization, which is a practice approach for economic relevant educational case study.

Source: International Conference on Computer Information and Big Data Applications (CIBDA)

DOI: 10.1145/3746709.3746763

Authors: Xiaohu Fan, Yue Zhang, Zhi Li, Fan Li, Yibin Min, Shun Zhang

Institutions: Wuhan City Polytechnic, Wuhan Donghu University, Wuhan College

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Funder: Central China Normal University