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Table of Contents
Agenda Background (~5 minutes) Prefix Tuning (~25 minutes) Motivation Methodology Results Additional Experiments Discussion Prompt Tuning Introduction Prompt tuning Design Decisions Compare with previous methods Resilience Prompt ensembling Interpretability Background: Fine Tuning Pretrain a language model on task Devlin et al. 2019 Background: In-context Learning Pretrain a language model on task (LM) fromage Brown et al. 2020 Background: Parameter-efficient Fine tuning With standard fine-tuning, we need to make a new copy of the model for each task.
Summary
The document discusses the concept of prompt as a parameter-efficient fine-tuning method. It explores the motivation, methodology, results, and additional experiments related to prefix tuning and prompt tuning. The paper compares different approaches and highlights the benefits of prompt tuning in terms of efficiency and performance. It also delves into aspects such as resilience to domain shift, prompt ensembling, and interpretability in the context of fine-tuning models for various natural language processing tasks.