Unsupervised Calibration through Prior Adaptation for Text Classification using Large Language Models

• URL: http://arxiv.org/abs/2307.06713v3
• Date: Wed, 9 Aug 2023 07:40:43 GMT
• Title: Unsupervised Calibration through Prior Adaptation for Text Classification using Large Language Models
• Authors: Lautaro Estienne, Luciana Ferrer, Mat\'ias Vera, Pablo Piantanida
• Abstract summary: We propose an approach to adapt the prior class distribution to perform text classification tasks without the need for labelled samples. Results show that these methods outperform the un-adapted model for different number of training shots in the prompt.
• Score: 37.39843935632105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A wide variety of natural language tasks are currently being addressed with large-scale language models (LLMs). These models are usually trained with a very large amount of unsupervised text data and adapted to perform a downstream natural language task using methods like fine-tuning, calibration or in-context learning. In this work, we propose an approach to adapt the prior class distribution to perform text classification tasks without the need for labelled samples and only few in-domain sample queries. The proposed approach treats the LLM as a black box, adding a stage where the model posteriors are calibrated to the task. Results show that these methods outperform the un-adapted model for different number of training shots in the prompt and a previous approach were calibration is performed without using any adaptation data.

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