10 papers
Zero-RAG: Towards Retrieval-Augmented Generation with Zero Redundant Knowledge
Qi Luo, Xiaonan Li, Junqi Dai +2
Retrieval-Augmented Generation has shown remarkable results to address Large Language Models' hallucinations, which usually uses a large external corpus to supplement knowledge to…
Towards Global Retrieval Augmented Generation: A Benchmark for Corpus-Level Reasoning
Qi Luo, Xiaonan Li, Tingshuo Fan +2
Retrieval-augmented generation (RAG) has emerged as a leading approach to reducing hallucinations in large language models (LLMs). Current RAG evaluation benchmarks primarily focus…
MARAG-R1: Beyond Single Retriever via Reinforcement-Learned Multi-Tool Agentic Retrieval
Qi Luo, Xiaonan Li, Yuxin Wang +4
Large Language Models (LLMs) excel at reasoning and generation but are inherently limited by static pretraining data, resulting in factual inaccuracies and weak adaptability to new…
R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning
Yuan Li, Qi Luo, Xiaonan Li +7
Retrieval-Augmented Generation (RAG) integrates external knowledge with Large Language Models (LLMs) to enhance factual correctness and mitigate hallucination. However, dense retri…
Case2Code: Scalable Synthetic Data for Code Generation
Yunfan Shao, Linyang Li, Yichuan Ma +11
Large Language Models (LLMs) have shown outstanding breakthroughs in code generation. Recent work improves code LLMs by training on synthetic data generated by some powerful LLMs,…
Understanding the Role of LLMs in Multimodal Evaluation Benchmarks
Botian Jiang, Lei Li, Xiaonan Li +5
The rapid advancement of Multimodal Large Language Models (MLLMs) has been accompanied by the development of various benchmarks to evaluate their capabilities. However, the true na…