2 citations · 2 across the 1 of their papers we have counts for
5 papers
Olmo 3
Team Olmo, :, Allyson Ettinger +66
We introduce Olmo 3, a family of state-of-the-art, fully-open language models at the 7B and 32B parameter scales. Olmo 3 model construction targets long-context reasoning, function…
Shrinking the Generation-Verification Gap with Weak Verifiers
Jon Saad-Falcon, E. Kelly Buchanan, Mayee F. Chen +10
Verifiers can improve language model capabilities by scoring and ranking responses from generated candidates. Currently, high-quality verifiers are either unscalable (e.g., humans)…
Aioli: A Unified Optimization Framework for Language Model Data Mixing
Mayee F. Chen, Michael Y. Hu, Nicholas Lourie +2
Language model performance depends on identifying the optimal mixture of data groups to train on (e.g., law, code, math). Prior work has proposed a diverse set of methods to effici…
Smoothie: Label Free Language Model Routing
Neel Guha, Mayee F. Chen, Trevor Chow +2
Large language models (LLMs) are increasingly used in applications where LLM inputs may span many different tasks. Recent work has found that the choice of LLM is consequential, an…
Cookbook: A framework for improving LLM generative abilities via programmatic data generating templates
Avanika Narayan, Mayee F. Chen, Kush Bhatia +1
Fine-tuning large language models (LLMs) on instruction datasets is a common way to improve their generative capabilities. However, instruction datasets can be expensive and time-c…