3 citations · 6 across the 9 of their papers we have counts for
9 papers
From Content Generation to Learning Support: Pedagogy-Guided Generative Video Tutors for STEM Learning
Xinchen Ma, Shuimu Wang, Gaole He +4
Generative AI enables scalable production of educational videos, but current systems largely focus on producing visually coherent content rather than supporting learning. As a resu…
Progress-SQL: Improving Reinforcement Learning for Text-to-SQL via Progressive Rewards
Shihao Zhang, Xiaoman Wang, Yuan Liu +2
Reinforcement learning has recently shown promise in improving large language models for Text-to-SQL generation, yet existing methods typically optimize one-shot rewards defined ov…
Skill0.5: Joint Skill Internalization and Utilization for Out-of-Distribution Generalization in Agentic Reinforcement Learning
Jiapeng Zhu, Jianxiang Yu, Yibo Zhao +5
Equipping large language models with explicit skills has emerged as a promising paradigm for enabling autonomous agents to solve complex tasks. Agent skills can be inherently divid…
TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generation
Wenbiao Tao, Xinyuan Li, Yunshi Lan +1
Retrieval-Augmented Generation enhances language models by retrieving external knowledge to support informed and grounded responses. However, traditional RAG methods rely on fragme…
Multi-turn Natural Language to Graph Query Language Translation
Yuanyuan Liang, Lei Pan, Tingyu Xie +2
In recent years, research on transforming natural language into graph query language (NL2GQL) has been increasing. Most existing methods focus on single-turn transformation from NL…
NAT-NL2GQL: A Novel Multi-Agent Framework for Translating Natural Language to Graph Query Language
Yuanyuan Liang, Tingyu Xie, Gan Peng +3
The emergence of Large Language Models (LLMs) has revolutionized many fields, not only traditional natural language processing (NLP) tasks. Recently, research on applying LLMs to t…
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt Tuning
Jiapeng Zhu, Zichen Ding, Jianxiang Yu +3
The advent of the "pre-train, prompt" paradigm has recently extended its generalization ability and data efficiency to graph representation learning, following its achievements in…
Unsupervised Text Style Transfer via LLMs and Attention Masking with Multi-way Interactions
Lei Pan, Yunshi Lan, Yang Li +1
Unsupervised Text Style Transfer (UTST) has emerged as a critical task within the domain of Natural Language Processing (NLP), aiming to transfer one stylistic aspect of a sentence…
Aligning Large Language Models to a Domain-specific Graph Database for NL2GQL
Yuanyuan Liang, Keren Tan, Tingyu Xie +4
Graph Databases (Graph DB) find extensive application across diverse domains such as finance, social networks, and medicine. Yet, the translation of Natural Language (NL) into the…