most citedRetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation

2 citations · 3 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CL2025

Neuro-Symbolic Query Compiler

Yuyao Zhang, Zhicheng Dou, Xiaoxi Li +5

Precise recognition of search intent in Retrieval-Augmented Generation (RAG) systems remains a challenging goal, especially under resource constraints and for complex queries with…

cs.CL2025★ 1 cited

Hierarchical Document Refinement for Long-context Retrieval-augmented Generation

Jiajie Jin, Xiaoxi Li, Guanting Dong +6

Real-world RAG applications often encounter long-context input scenarios, where redundant information and noise results in higher inference costs and reduced performance. To addres…

cs.CL2024★ 2 cited

RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation

Xiaoxi Li, Jiajie Jin, Yujia Zhou +4

Large language models (LLMs) exhibit remarkable generative capabilities but often suffer from hallucinations. Retrieval-augmented generation (RAG) offers an effective solution by i…