1 citations · 1 across the 2 of their papers we have counts for
18 papers
Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data
Ofir Arviv, Kristjan Greenewald, Yotam Perlitz +3
The inherent rigidity of fixed-size benchmarks makes them an inefficient tool for model evaluation. Diverse evaluation objectives, including model ranking, model selection and test…
Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection
Yihao Xue, Kristjan Greenewald, Youssef Mroueh +1
Large Language Models (LLMs) often hallucinate, limiting their reliability in sensitive applications. In black-box settings, several self-consistency-based techniques have been pro…
Distributional Process Reward Models: Calibrated Prediction of Future Rewards via Conditional Optimal Transport
Rachel Ma, Dylan Hadfield-Menell, Kristjan Greenewald
Inference-time scaling methods rely on Process Reward Models (PRMs), which are often poorly calibrated and overestimate success probabilities. We propose, to our knowledge, the fir…
Finite sample rates of convergence for the Bigraphical and Tensor graphical Lasso estimators
Shuheng Zhou, Kristjan Greenewald
Many modern datasets exhibit dependencies among observations as well as variables. A decade ago, Kalaitzis et. al. (2013) proposed the Bigraphical Lasso, an estimator for precision…
Efficient Multi-Adapter LLM Serving via Cross-Model KV-Cache Reuse with Activated LoRA
Allison Li, Kristjan Greenewald, Thomas Parnell +1
Modern large language model (LLM) systems increasingly rely on multi-turn pipelines that are composed of multiple task-specific adapters, yet existing serving frameworks remain ine…
Know What You Don't Know: Uncertainty Calibration of Process Reward Models
Young-Jin Park, Kristjan Greenewald, Kaveh Alim +2
Process reward models (PRMs) play a central role in guiding inference-time scaling algorithms for large language models (LLMs). However, we observe that even state-of-the-art PRMs…