36 citations · 74 across the 8 of their papers we have counts for
9 papers
Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes
Justin Bauer, Thomas Walshe, Derek Pham +4
Fine-tuning Large Language Models (LLMs) typically relies on large quantities of high-quality annotated data, or questions with well-defined ground truth answers in the case of Rei…
The Hitchhikers Guide to Rubric Quality Understanding and Enrichment
Zhengyang Qi, Ankit Aich, Charles Dickens +7
Rubrics distill notions of expert quality and measure agent performance. However, the quality of rubrics themselves have not been systematically measured and are often left to down…
Automating Benchmark Design
Amanda Dsouza, Harit Vishwakarma, Zhengyang Qi +6
The rapid progress and widespread deployment of LLMs and LLM-powered agents has outpaced our ability to evaluate them. Hand-crafted, static benchmarks are the primary tool for asse…
Multi-Resolution Weak Supervision for Sequential Data
Frederic Sala, Paroma Varma, Jason Fries +8
Since manually labeling training data is slow and expensive, recent industrial and scientific research efforts have turned to weaker or noisier forms of supervision sources. Howeve…
Scene Graph Prediction with Limited Labels
Vincent S. Chen, Paroma Varma, Ranjay Krishna +3
Visual knowledge bases such as Visual Genome power numerous applications in computer vision, including visual question answering and captioning, but suffer from sparse, incomplete…
Learning Dependency Structures for Weak Supervision Models
Paroma Varma, Frederic Sala, Ann He +2
Labeling training data is a key bottleneck in the modern machine learning pipeline. Recent weak supervision approaches combine labels from multiple noisy sources by estimating thei…