4 papers
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 (…
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…
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…
Finetuning Language Models to Emit Linguistic Expressions of Uncertainty
Arslan Chaudhry, Sridhar Thiagarajan, Dilan Gorur
Large language models (LLMs) are increasingly employed in information-seeking and decision-making tasks. Despite their broad utility, LLMs tend to generate information that conflic…