2 citations · 3 across the 7 of their papers we have counts for
9 papers
ACE-GraphRAG: Agentic Context Engineering for Hierarchical GraphRAG
Yongfeng Huang, Yuren Lai, Ruiying Chen +3
Hierarchical Graph Retrieval-Augmented Generation (GraphRAG) organizes corpus knowledge at multiple levels of granularity, yet fixed context construction may fail to translate thes…
TaxAgent: How Large Language Model Designs Fiscal Policy
Jizhou Wang, Xiaodan Fang, Lei Huang +1
Economic inequality is a global challenge, intensifying disparities in education, healthcare, and social stability. Traditional systems like the U.S. federal income tax reduce ineq…
SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications
Jinyang Li, Xiaolong Li, Ge Qu +17
Resolution of complex SQL issues persists as a significant bottleneck in real-world database applications. Current Large Language Models (LLMs), while adept at text-to-SQL translat…
Retrieval-Augmented Generation with Hierarchical Knowledge
Haoyu Huang, Yongfeng Huang, Junjie Yang +5
Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks. However, existing RAG…
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning
Jiahao Lai, Jiaqi Li, Jian Xu +6
Federated Learning (FL) offers a decentralized approach to model training, where data remains local and only model parameters are shared between the clients and the central server.…
SETTP: Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning
Chunzhen Jin, Yongfeng Huang, Yaqi Wang +2
Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources…
Mitigate Position Bias in Large Language Models via Scaling a Single Dimension
Yijiong Yu, Huiqiang Jiang, Xufang Luo +6
Large Language Models (LLMs) are increasingly applied in various real-world scenarios due to their excellent generalization capabilities and robust generative abilities. However, t…
ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses
Chang Zong, Yuyan Chen, Weiming Lu +4
Large Language Models (LLMs) have demonstrated efficacy in various linguistic applications, including question answering and controlled text generation. However, studies into their…
Training With "Paraphrasing the Original Text" Teaches LLM to Better Retrieve in Long-context Tasks
Yijiong Yu, Yongfeng Huang, Zhixiao Qi +1
As Large Language Models (LLMs) continue to evolve, more are being designed to handle long-context inputs. Despite this advancement, most of them still face challenges in accuratel…