3 papers
cs.CL2025
Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction
Xinhe Li, Jiajun Liu, Peng Wang
Recent studies have demonstrated that Large Language Models (LLMs) have strong mathematical reasoning abilities but rely on hundreds of billions of parameters. To tackle the challe…
cs.AI2025
Unlearning of Knowledge Graph Embedding via Preference Optimization
Jiajun Liu, Wenjun Ke, Peng Wang +5
Existing knowledge graphs (KGs) inevitably contain outdated or erroneous knowledge that needs to be removed from knowledge graph embedding (KGE) models. To address this challenge,…
cs.AI2024
Towards Continual Knowledge Graph Embedding via Incremental Distillation
Jiajun Liu, Wenjun Ke, Peng Wang +5
Traditional knowledge graph embedding (KGE) methods typically require preserving the entire knowledge graph (KG) with significant training costs when new knowledge emerges. To addr…