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

9 citations · 11 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2025

DualEdit: Dual Editing for Knowledge Updating in Vision-Language Models

Zhiyi Shi, Binjie Wang, Chongjie Si +3

Model editing aims to efficiently update a pre-trained model's knowledge without the need for time-consuming full retraining. While existing pioneering editing methods achieve prom…

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.CL20241 cited

Halu-J: Critique-Based Hallucination Judge

Binjie Wang, Steffi Chern, Ethan Chern +1

Large language models (LLMs) frequently generate non-factual content, known as hallucinations. Existing retrieval-augmented-based hallucination detection approaches typically addre…

cs.CL20241 cited

BeHonest: Benchmarking Honesty in Large Language Models

Steffi Chern, Zhulin Hu, Yuqing Yang +5

Previous works on Large Language Models (LLMs) have mainly focused on evaluating their helpfulness or harmlessness. However, honesty, another crucial alignment criterion, has recei…

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…