most citedRAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

9 citations · 9 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CL20249 cited

RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

Dongyu Ru, Lin Qiu, Xiangkun Hu +15

Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging external knowledge, a comprehensive evaluation of RAG systems is still challenging due to th…

cs.IR2024

OpenResearcher: Unleashing AI for Accelerated Scientific Research

Yuxiang Zheng, Shichao Sun, Lin Qiu +13

The rapid growth of scientific literature imposes significant challenges for researchers endeavoring to stay updated with the latest advancements in their fields and delve into new…

cs.AI2024

FRoG: Evaluating Fuzzy Reasoning of Generalized Quantifiers in Large Language Models

Yiyuan Li, Shichao Sun, Pengfei Liu

Fuzzy reasoning is vital due to the frequent use of imprecise information in daily contexts. However, the ability of current large language models (LLMs) to handle such reasoning r…

cs.CL2024

OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI

Zhen Huang, Zengzhi Wang, Shijie Xia +25

The evolution of Artificial Intelligence (AI) has been significantly accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showc…

cs.CL2024

Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization

Shichao Sun, Ruifeng Yuan, Ziqiang Cao +2

Large language models (LLMs) have demonstrated the capacity to improve summary quality by mirroring a human-like iterative process of critique and refinement starting from the init…