2 citations · 2 across the 5 of their papers we have counts for
6 papers
Decentralized LLM Workflows via Sequential Auctions
Ian Gemp, Shubhendu Trivedi, Adam Fisch +7
We present a scalable framework for solving multi-step tasks through a decentralized market of self-interested agents. Prior theoretical work demonstrated that agents organized thr…
Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation
Adam Fisch, Shubhendu Trivedi, Fantine Huot +5
Heterogeneous AI systems composed of multiple models, architectures, harnesses, or inference-time settings can improve quality and efficiency by routing queries to the specialist w…
CollabEval: Statistically Efficient Collaborative Model Evaluation via Matrix Completion
Adam Fisch, Daniel Deutsch, Joshua Maynez +5
Evaluating generative AI models is a routine, but resource-intensive, process that is conducted over and over again during the course of model development. In this work, we propose…
Multiple-Prediction-Powered Inference
Charlie Cowen-Breen, Alekh Agarwal, Stephen Bates +4
Statistical estimation often involves tradeoffs between expensive, high-quality measurements and a variety of lower-quality proxies. We introduce Multiple-Prediction-Powered Infere…
Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation
Adam Fisch, Joshua Maynez, R. Alex Hofer +3
Prediction-powered inference (PPI) is a method that improves statistical estimates based on limited human-labeled data. PPI achieves this by combining small amounts of human-labele…
Bayesian Prediction-Powered Inference
R. Alex Hofer, Joshua Maynez, Bhuwan Dhingra +3
Prediction-powered inference (PPI) is a method that improves statistical estimates based on limited human-labeled data. Specifically, PPI methods provide tighter confidence interva…