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

**Authors:** David Peer, Philemon Schöpf, Volckmar Nebendahl, Alexander Rietzler, Sebastian Stabinger  
**arXiv:** [2402.03848](https://arxiv.org/abs/2402.03848)  
**Date:** 2024-02-06  
**Summary page:** https://www.summarizepaper.com/fr/paper/2402.03848v1/

## 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 3 different GLLMs 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 15 out of 21 cases, SFT outperforms other techniques and improves the state-of-the-art, sometimes by as much as $15$ percentage points. Sources are available at https://github.com/deepopinion/anls_star_metric