3 citations · 6 across the 9 of their papers we have counts for
4 papers · 1 filter
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…