ANLS* -- A Universal Document Processing Metric for Generative Large Language Models

• URL: http://arxiv.org/abs/2402.03848v7
• Date: Tue, 27 Aug 2024 08:33:29 GMT
• Title: ANLS* -- A Universal Document Processing Metric for Generative Large Language Models
• Authors: David Peer, Philemon Schöpf, Volckmar Nebendahl, Alexander Rietzler, Sebastian Stabinger,
• Abstract summary: This paper introduces a new metric for evaluating generative models called ANLS*. The ANLS* metric extends existing ANLS metrics as a drop-in-replacement and is still compatible with previously reported ANLS scores. We also benchmark a novel approach to generate prompts for documents, called SFT, against other prompting techniques such as LATIN.
• Score: 40.94659575657584
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditionally, discriminative models have been the predominant choice for tasks like document classification and information extraction. These models make predictions that fall into a limited number of predefined classes, facilitating a binary true or false evaluation and enabling the direct calculation of metrics such as the F1 score. However, recent advancements in generative large language models (GLLMs) have prompted a shift in the field due to their enhanced zero-shot capabilities, which eliminate the need for a downstream dataset and computationally expensive fine-tuning. However, evaluating GLLMs presents a challenge as the binary true or false evaluation used for discriminative models is not applicable to the predictions made by GLLMs. This paper introduces a new metric for generative models called ANLS* for evaluating a wide variety of tasks, including information extraction and classification tasks. The ANLS* metric extends existing ANLS metrics as a drop-in-replacement and is still compatible with previously reported ANLS scores. An evaluation of 7 different datasets, and more than 10 different GLLMs together with 3 different prompting methods using the ANLS* metric is also provided, demonstrating the importance of the proposed metric. We also benchmark a novel approach to generate prompts for documents, called SFT, against other prompting techniques such as LATIN. In almost all cases, SFT outperforms other techniques and improves the state-of-the-art, sometimes by as much as $10$ percentage points. Sources are available at https://github.com/deepopinion/anls_star_metric

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