9 papers
Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on Knowledge Graph Question Answering
Runxuan Liu, Bei Luo, Jiaqi Li +5
Large language models (LLMs) have shown remarkable capabilities in natural language processing. However, in knowledge graph question answering tasks (KGQA), there remains the issue…
CE-GOCD: Central Entity-Guided Graph Optimization for Community Detection to Augment LLM Scientific Question Answering
Jiayin Lan, Jiaqi Li, Baoxin Wang +5
Large Language Models (LLMs) are increasingly used for question answering over scientific research papers. Existing retrieval augmentation methods often rely on isolated text chunk…
Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models
Runxuan Liu, Xianhao Ou, Xinyan Ma +13
Long Chain-of-Thought (LCoT), achieved by Reinforcement Learning with Verifiable Rewards (RLVR), has proven effective in enhancing the reasoning capabilities of Large Language Mode…
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems
Zekun Zhou, Xiaocheng Feng, Lei Huang +11
Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development…
RE: Improving Chinese Grammatical Error Correction via Retrieving Appropriate Examples with Explanation
Baoxin Wang, Yumeng Luo, Yixuan Wang +3
The primary objective of Chinese grammatical error correction (CGEC) is to detect and correct errors in Chinese sentences. Recent research shows that large language models (LLMs) h…
NLP-AKG: Few-Shot Construction of NLP Academic Knowledge Graph Based on LLM
Jiayin Lan, Jiaqi Li, Baoxin Wang +4
Large language models (LLMs) have been widely applied in question answering over scientific research papers. To enhance the professionalism and accuracy of responses, many studies…