1 citations · 2 across the 5 of their papers we have counts for
5 papers
Rethinking Rubric Generation for Improving LLM Judge and Reward Modeling for Open-ended Tasks
William F. Shen, Xinchi Qiu, Chenxi Whitehouse +6
Recently, rubrics have been used to guide LLM judges in capturing subjective, nuanced, multi-dimensional human preferences, and have been extended from evaluation to reward signals…
Balanced Accuracy: The Right Metric for Evaluating LLM Judges -- Explained through Youden's J statistic
Stephane Collot, Colin Fraser, Justin Zhao +3
Rigorous evaluation of large language models (LLMs) relies on comparing models by the prevalence of desirable or undesirable behaviors, such as task pass rates or policy violations…
The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes
Redacted by arXiv
This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…
Training AI Co-Scientists Using Rubric Rewards
Shashwat Goel, Rishi Hazra, Dulhan Jayalath +8
AI co-scientists are emerging as a tool to assist human researchers in achieving their research goals. A crucial feature of these AI co-scientists is the ability to generate a rese…
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
Maximilian Lam, Jeff Johnson, Wenjie Xiong +11
On-device machine learning (ML) inference can enable the use of private user data on user devices without revealing them to remote servers. However, a pure on-device solution to pr…