10 papers
Scaling Small Agents Through Strategy Auctions
Lisa Alazraki, William F. Shen, Yoram Bachrach +1
Small language models are increasingly viewed as a promising, cost-effective approach to agentic AI, with proponents claiming they are sufficiently capable for agentic workflows. H…
Repetition Mismatch: Why Data Mixture Experiments Don't Scale and How to Fix Them
Kevin Zhou, Lisa Alazraki, Kris Cao +1
Pre-training data mixtures are commonly tuned by running small-scale experiments and extrapolating to the target training budget. When high-quality data is scarce and must be repea…
AgentCoMa: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarios
Lisa Alazraki, Lihu Chen, Ana Brassard +3
Large Language Models (LLMs) have achieved high accuracy on complex commonsense and mathematical problems that involve the composition of multiple reasoning steps. However, current…
Rethinking Rubric Generation for Improving LLM Judge and Reward Modeling for Open-ended Tasks
William F. Shen, Xinchi Qiu, Chenxi Whitehouse +6
Recently, rubrics have been used to guide LLM judges in capturing subjective, nuanced, multi-dimensional human preferences, and have been extended from evaluation to reward signals…
Reverse Engineering Human Preferences with Reinforcement Learning
Lisa Alazraki, Tan Yi-Chern, Jon Ander Campos +3
The capabilities of Large Language Models (LLMs) are routinely evaluated by other LLMs trained to predict human preferences. This framework--known as LLM-as-a-judge--is highly scal…
Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection
Joe Stacey, Lisa Alazraki, Aran Ubhi +3
We investigate the robustness of fine-tuned Large Language Models (LLMs) for the task of Natural Language Inference (NLI), finding that the in-distribution gains from fine-tuning c…