Мягкие Подсказки
Related Papers
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts
AbstractThis work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts—small prefix embedding vectors pre-trained …...
MetaPrompting: Learning to Learn Better Prompts
AbstractPrompting method is regarded as one of the crucial progress for few-shot nature language processing. Recent research on prompting moves from discrete tokens based “hard prompts” to continuous “soft prompts”, …...
On Transferability of Prompt Tuning for Natural Language Processing
AbstractPrompt tuning (PT) is a promising parameter-efficient method to utilize extremely large pre-trained language models (PLMs), which can achieve comparable performance to full-parameter fine-tuning by only tuning a few soft …...
Prompt-based Conservation Learning for Multi-hop Question Answering
AbstractMulti-hop question answering (QA) requires reasoning over multiple documents to answer a complex question and provide interpretable supporting evidence. However, providing supporting evidence is not enough to demonstrate that a …...
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning
AbstractPrompting has shown impressive success in enabling large pre-trained language models (LMs) to perform diverse NLP tasks, especially with only few downstream data. Automatically finding the optimal prompt for each …...
Scalable Prompt Generation for Semi-supervised Learning with Language Models
AbstractPrompt-based learning methods in semi-supervised learning (SSL) settings have been shown to be effective on multiple natural language understanding (NLU) datasets and tasks in the literature. However, manually designing multiple …...