6 papers
J-Miner: Recovering Executable Decision Knowledge from Language-Model Classifiers
Yunfan Gao, Xinyi Huang, Tao Sheng +3
Large language models can be fine-tuned into specialized classifiers that perform well across diverse text tasks and make complex judgments, but they typically expose only final la…
DS2SC-Agent: A Multi-Agent Automated Pipeline for Rapid Chiplet Model Generation
Yiwei Wu, Yifan Wu, Yunhao Xiong +6
Constructing behavioral-level chiplet models (e.g., SystemC) is crucial for early-stage heterogeneous architecture exploration. Traditional manual modeling is notoriously time-cons…
Synergizing RAG and Reasoning: A Systematic Review
Yunfan Gao, Yun Xiong, Yijie Zhong +3
Recent breakthroughs in large language models (LLMs), particularly in reasoning capabilities, have propelled Retrieval-Augmented Generation (RAG) to unprecedented levels. By synerg…
U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-In-A-Haystack
Yunfan Gao, Yun Xiong, Wenlong Wu +3
Recent advancements in Large Language Models (LLMs) have expanded their context windows to unprecedented lengths, sparking debates about the necessity of Retrieval-Augmented Genera…
Preference-Guided Refactored Tuning for Retrieval Augmented Code Generation
Xinyu Gao, Yun Xiong, Deze Wang +4
Retrieval-augmented code generation utilizes Large Language Models as the generator and significantly expands their code generation capabilities by providing relevant code, documen…
Modular RAG: Transforming RAG Systems into LEGO-like Reconfigurable Frameworks
Yunfan Gao, Yun Xiong, Meng Wang +1
Retrieval-augmented Generation (RAG) has markedly enhanced the capabilities of Large Language Models (LLMs) in tackling knowledge-intensive tasks. The increasing demands of applica…