Jun 26, 2023 · We propose a multi-mask prompt-based approach with Multi-Mask Label Mapping (MMLM) to reduce the impact of misleading lexical cues by allowing ...
Prompt-based Learning has shown significant success in few- shot classification. The mainstream approach is to concate- nate a template for the input text ...
Feb 7, 2023 · We propose a multi-mask prompt-based approach with Multi-Mask Label Mapping (MMLM) to reduce the impact of misleading lexical cues by allowing ...
To satisfy the conditions of few-shot learning, an instance augmentation approach for the cloze-type model is proposed and the misleading cues are gradually ...
In this paper, we investigate the in-context learning ability of retrieval-augmented encoder-decoder language models. We first conduct a comprehensive analysis ...
Feb 20, 2024 · This method transforms a text input into a masked language modeling prompt using a template, queries a fine-tuned language model to fill in the ...
Jul 20, 2023 · Scope of Reproducibility — We reproduce the results in the paper Automatic Multi‐Label. Prompting: Simple and Interpretable Few‐Shot ...
Prompt-based learning has achieved excellent performance in few-shot learning by mapping the outputs of the pre-trained language model to the labels with ...
Sep 12, 2024 · To investigate this, we breakdown and analyze the effect of various components of the prompting strategies in SOTA VLM-based MLR methods which ...
Similar lecture. Multi-Mask Label Mapping for Prompt-based Learning. poster. Multi-Mask Label Mapping for Prompt-based Learning. AAAI 2023. +3. JK. Junfan Chen.