3 papers
cs.LG2026
Improving Latent Generalization Using Test-time Compute
Arslan Chaudhry, Sridhar Thiagarajan, Andrew Lampinen
Language Models (LMs) exhibit two distinct mechanisms for knowledge acquisition: in-weights learning (i.e., encoding information within the model weights) and in-context learning (…
cs.CY2025
Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach
Irina Jurenka, Markus Kunesch, Kevin R. McKee +71
A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about…
cs.CL2025
Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models
Yinlam Chow, Guy Tennenholtz, Izzeddin Gur +7
Recent studies have indicated that effectively utilizing inference-time compute is crucial for attaining better performance from large language models (LLMs). In this work, we prop…