EXnet: Efficient In-context Learning for Data-less Text classification

• URL: http://arxiv.org/abs/2305.14622v1
• Date: Wed, 24 May 2023 01:40:57 GMT
• Title: EXnet: Efficient In-context Learning for Data-less Text classification
• Authors: Debaditya Shome, Kuldeep Yadav
• Abstract summary: We present EXnet, a model specifically designed to perform in-context learning without limitations on the number of examples. We argue that in-context learning is an effective method to increase task accuracy, and providing examples facilitates cross-task generalization. With extensive experiments, we show that even our smallest model (15M parameters) generalizes to several unseen classification tasks and domains.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large pre-trained language models (PLMs) have made significant progress in encoding world knowledge and spawned a new set of learning paradigms including zero-shot, few-shot, and in-context learning. Many language tasks can be modeled as a set of prompts (for example, is this text about geography?) and language models can provide binary answers, i.e., Yes or No. There is evidence to suggest that the next-word prediction used by many PLMs does not align well with zero-shot paradigms. Therefore, PLMs are fine-tuned as a question-answering system. In-context learning extends zero-shot learning by incorporating prompts and examples, resulting in increased task accuracy. Our paper presents EXnet, a model specifically designed to perform in-context learning without any limitations on the number of examples. We argue that in-context learning is an effective method to increase task accuracy, and providing examples facilitates cross-task generalization, especially when it comes to text classification tasks. With extensive experiments, we show that even our smallest model (15M parameters) generalizes to several unseen classification tasks and domains.

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