4 citations · 4 across the 12 of their papers we have counts for
4 papers · 1 filter
MLE-Smith: Scaling MLE Tasks with Automated Multi-Agent Pipeline
Rushi Qiang, Yuchen Zhuang, Anikait Singh +4
While Language Models (LMs) have made significant progress in automating machine learning engineering (MLE), the acquisition of high-quality MLE training data is significantly cons…
Towards Better Instruction Following Retrieval Models
Yuchen Zhuang, Aaron Trinh, Rushi Qiang +4
Modern information retrieval (IR) models, trained exclusively on standard <query, passage> pairs, struggle to effectively interpret and follow explicit user instructions. We introd…
MLE-Dojo: Interactive Environments for Empowering LLM Agents in Machine Learning Engineering
Rushi Qiang, Yuchen Zhuang, Yinghao Li +8
We introduce MLE-Dojo, a Gym-style framework for systematically reinforcement learning, evaluating, and improving autonomous large language model (LLM) agents in iterative machine…
Language Model Uncertainty Quantification with Attention Chain
Yinghao Li, Rushi Qiang, Lama Moukheiber +1
Accurately quantifying a large language model's (LLM) predictive uncertainty is crucial for judging the reliability of its answers. While most existing research focuses on short, d…