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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
MAP's not dead yet: Uncovering true language model modes by conditioning away degeneracy
Davis Yoshida, Kartik Goyal, Kevin Gimpel
It has been widely observed that exact or approximate MAP (mode-seeking) decoding from natural language generation (NLG) models consistently leads to degenerate outputs (Holtzman e…
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…