9 citations · 23 across the 10 of their papers we have counts for
13 papers
CondenseGraph: Communication-Efficient Distributed GNN Training via On-the-Fly Graph Condensation
Zizhao Zhang, Yihan Xue, Haotian Zhu +3
Distributed Graph Neural Network (GNN) training suffers from substantial communication overhead due to the inherent neighborhood dependency in graph-structured data. This neighbor…
COMPASS: Enhancing Agent Long-Horizon Reasoning with Evolving Context
Guangya Wan, Mingyang Ling, Xiaoqi Ren +3
Long-horizon tasks that require sustained reasoning and multiple tool interactions remain challenging for LLM agents: small errors compound across steps, and even state-of-the-art…
SkillVerse : Assessing and Enhancing LLMs with Tree Evaluation
Yufei Tian, Jiao Sun, Nanyun Peng +1
As language models evolve to tackle complex, multifaceted tasks, their evaluation must adapt to capture this intricacy. A granular, skill-specific understanding of model capabiliti…
RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models
Bang An, Shiyue Zhang, Mark Dredze
Efforts to ensure the safety of large language models (LLMs) include safety fine-tuning, evaluation, and red teaming. However, despite the widespread use of the Retrieval-Augmented…
The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input
Alon Jacovi, Andrew Wang, Chris Alberti +23
We introduce FACTS Grounding, an online leaderboard and associated benchmark that evaluates language models' ability to generate text that is factually accurate with respect to giv…
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…