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cs.CL2024
Making the Most of your Model: Methods for Finetuning and Applying Pretrained Transformers
Davis Yoshida
This thesis provides methods and analysis of models which make progress on this goal. The techniques outlined are task agnostic, and should provide benefit when used with nearly an…
cs.CL2024
Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers
Lütfi Kerem Senel, Besnik Fetahu, Davis Yoshida +5
Recommender systems are widely used to suggest engaging content, and Large Language Models (LLMs) have given rise to generative recommenders. Such systems can directly generate ite…
cs.CL2021
Reconsidering the Past: Optimizing Hidden States in Language Models
Davis Yoshida, Kevin Gimpel
We present Hidden-State Optimization (HSO), a gradient-based method for improving the performance of transformer language models at inference time. Similar to dynamic evaluation (K…