6 papers
Training-Free Test-Time Contrastive Learning for Large Language Models
Kaiwen Zheng, Kai Zhou, Jinwu Hu +3
Large language models (LLMs) demonstrate strong reasoning capabilities, but their performance often degrades under distribution shift. Existing test-time adaptation (TTA) methods r…
Plan Then Retrieve: Reinforcement Learning-Guided Complex Reasoning over Knowledge Graphs
Yanlin Song, Ben Liu, VÃctor Gutiérrez-Basulto +5
Knowledge Graph Question Answering aims to answer natural language questions by reasoning over structured knowledge graphs. While large language models have advanced KGQA through t…
Towards Explainable Temporal Reasoning in Large Language Models: A Structure-Aware Generative Framework
Zihao Jiang, Ben Liu, Miao Peng +4
While large language models (LLMs) show great potential in temporal reasoning, most existing work focuses heavily on enhancing performance, often neglecting the explainable reasoni…
One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction
Ben Liu, Jihan Zhang, Fangquan Lin +2
Large language models (LLMs) have been increasingly employed in various intelligent educational systems, simulating human tutors to facilitate effective human-machine interaction.…
SymAgent: A Neural-Symbolic Self-Learning Agent Framework for Complex Reasoning over Knowledge Graphs
Ben Liu, Jihai Zhang, Fangquan Lin +3
Recent advancements have highlighted that Large Language Models (LLMs) are prone to hallucinations when solving complex reasoning problems, leading to erroneous results. To tackle…
Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion
Ben Liu, Jihai Zhang, Fangquan Lin +2
Large Language Models (LLMs) present massive inherent knowledge and superior semantic comprehension capability, which have revolutionized various tasks in natural language processi…