TEACH: A Contrastive Knowledge Adaptive Distillation Framework for Classical Chinese Understanding
Year: 2025
Type: conference-paper
Abstract: Traditional methods for processing classical Chinese typically segment language understanding into discrete tasks, which overlook crucial background information and reduce user engagement.Large language models (LLMs) provide integrated solutions, yet they entail high computational costs and risks of generating inaccurate historical information.To tackle these challenges, we propose a novel framework, TEACH (conTrastive knowlEdge Adaptive distillation with enhanCed Historical interpretability), which focuses on classical Chinese understanding by integrating word sense disambiguation with sentence translation.This integration leverages a confidenceannotated knowledge base and a step-by-step Chain-of-Thought prompting mechanism to minimize hallucinations and improve semantic analysis.Moreover, TEACH employs contrastive distillation learning to efficiently transfer capabilities from larger models to smaller ones (e.g., Qwen2-1.5B),addressing overly liberal translations.Additionally, we introduce an innovative generation evaluation metric using iterative word alignment, enhancing LLM performance assessments by distinguishing additional information and addressing excessive translation issues.Experiments conducted on real-world datasets validate TEACH's efficacy in classical Chinese educational scenarios.
Authors: Yuting Wei, Qi Meng, Yuanxing Xu, Bin Wu
Institution: Beijing University of Posts and Telecommunications
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4412886653