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arXiv · 2412.13859

Zero-Shot Prompting and Few-Shot Fine-Tuning: Revisiting Document Image Classification Using Large Language Models

Anna Scius-Bertrand·Michael Jungo·Lars Vögtlin·Jean-Marc Spat·Andreas Fischer
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Abstract

Classifying scanned documents is a challenging problem that involves image, layout, and text analysis for document understanding. Nevertheless, for certain benchmark datasets, notably RVL-CDIP, the state of the art is closing in to near-perfect performance when considering hundreds of thousands of training samples. With the advent of large language models (LLMs), which are excellent few-shot learners, the question arises to what extent the document classification problem can be addressed with only a few training samples, or even none at all. In this paper, we investigate this question in the context of zero-shot prompting and few-shot model fine-tuning, with the aim of reducing the need for human-annotated training samples as much as possible.

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Anna Scius-Bertrand, Michael Jungo, Lars Vögtlin, Jean-Marc Spat, Andreas Fischer. 2024-12-18. Zero-Shot Prompting and Few-Shot Fine-Tuning: Revisiting Document Image Classification Using Large Language Models. https://doi.org/10.1007/978-3-031-78495-8_10

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