3 citations · 5 across the 8 of their papers we have counts for
8 papers
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…
SEAGraph: Unveiling the Whole Story of Paper Review Comments
Jianxiang Yu, Jiaqi Tan, Zichen Ding +7
Peer review, as a cornerstone of scientific research, ensures the integrity and quality of scholarly work by providing authors with objective feedback for refinement. However, in t…
DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning
Kangyang Luo, Shuai Wang, Yexuan Fu +5
Federated Learning (FL) is a distributed machine learning scheme in which clients jointly participate in the collaborative training of a global model by sharing model information r…
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator
Kangyang Luo, Shuai Wang, Xiang Li +3
Federated Learning (FL) is gaining popularity as a distributed learning framework that only shares model parameters or gradient updates and keeps private data locally. However, FL…
Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis
Jianxiang Yu, Zichen Ding, Jiaqi Tan +10
In recent years, the rapid increase in scientific papers has overwhelmed traditional review mechanisms, resulting in varying quality of publications. Although existing methods have…
An LLM-Enhanced Adversarial Editing System for Lexical Simplification
Keren Tan, Kangyang Luo, Yunshi Lan +2
Lexical Simplification (LS) aims to simplify text at the lexical level. Existing methods rely heavily on annotated data, making it challenging to apply in low-resource scenarios. I…
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…