3 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.AI2025
Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation
D. Sculley, Will Cukierski, Phil Culliton +8
In this position paper, we observe that empirical evaluation in Generative AI is at a crisis point since traditional ML evaluation and benchmarking strategies are insufficient to m…
cs.CL2025★ 2 cited
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…
cs.CL2025★ 3 cited
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…