anls-star 0.0.3

A universal metric for Generative Large Language Models (GLLMs)

ANLS ★

🌟 A Universal Document Processing Metric for Generative Large Language Models 🌟

@misc{anls_star,
    title={ANLS* -- A Universal Document Processing Metric for Generative Large Language Models}, 
    author={David Peer and Philemon Schöpf and Volckmar Nebendahl and Alexander Rietzler and Sebastian Stabinger},
    year={2024},
    eprint={2402.03848},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

How to use the ANLS* score?

1. pip install anls_star
2. Add to your code

from anls_star import anls_score
anls = anls_score("Hello World", "Hello Wrld")
print(anls)

4. Thats it!

Supported Types

Simply copy this file to your project and import the anls_score function from it. Then call the function with the ground truth and the predictions.

The following types (and all combinations of it) are supported:

• String: To compare strings against each other using the normalized Levenshtein similarity.
• None: Sometimes questions are not answerable. With this type it can be checked, whether the model does not answer. Any answer other than None will be penalized.
• Tuple: Compare the given answer with each element in the tuple and select the element that produces the maximum ANLS* score. This is also provided by the classical ANLS metric.
• List: Sometimes it is required to information in the form of lists from a document. For example, extracting all purchased items found in an invoice. While the order is not important, the list should contain all items. Note that the same item can occur multiple times in lists. Hungarian matching is used to compare the ground truth and the predicted list against each other. Both missing elements as well as hallucinated elements are penalized as previously introduced.
• Dict: For document information extraction it is usually required to extract key-value pairs. For example, when extracting the date and total value from an invoice. Missing keys as well as hallucinated keys are penalized.

Benchmarks

The following table shows the ANLS* score for the different models and prompt methods on different datasets. Note that we evaluate the models and prompt methods on 100 samples for single page datasets and 20 samples for multi page datasets in order to reduce the execution time and costs. Note that the provided validation set is used for the report.

Dataset	Method	gpt-3.5-turbo-16k	gpt-4-turbo	gpt-4-vision	gemini-pro	mistral-large	claude-3
DocVQA	Simple	0.586	0.607	0.759	0.586	0.445	0.768
	Latin Prompting	0.659	0.699	-	0.676	0.447	0.762
	SFT (Ours)	0.809	0.790	-	0.741	0.648	0.831
MPDocVQA	Simple	0.517	0.635	0.708	0.603	0.364	0.636
	Latin Prompting	0.499	0.739	-	0.502	0.335	0.438
	SFT (Ours)	0.734	0.781	-	0.616	0.476	0.575
Kleister Charity	Simple	0.490	0.743	0.751	0.583	0.652	0.800
	Latin Prompting	0.442	0.735	-	0.478	0.576	0.787
	SFT (Ours)	0.476	0.763	-	0.633	0.657	0.786
Kleister NDA	Simple	0.343	0.695	0.664	0.623	0.637	0.673
	Latin Prompting	0.434	0.705	-	0.599	0.624	0.67
	SFT (Ours)	0.355	0.703	-	0.552	0.641	0.677
SROIE	Simple	0.874	0.835	0.834	0.263	0.855	0.933
	Latin Prompting	0.849	0.851	-	0.371	0.863	0.926
	SFT (Ours)	0.893	0.873	-	0.288	0.905	0.949
VRDU AD Buy	Simple	0.402	0.553	0.640	0.510	0.386	0.577
	Latin Prompting	0.389	0.586	-	0.556	0.435	0.608
	SFT (Ours)	0.661	0.770	-	0.685	0.594	0.633
VRDU Registration	Simple	0.659	0.676	0.665	0.699	0.579	0.685
	Latin Prompting	0.693	0.673	-	0.740	0.587	0.715
	SFT (Ours)	0.723	0.711	-	0.720	0.639	0.705

How To Execute

1. Install all dependencies via pip install -r requirements_dev.txt
2. Setup the keys

• OpenAI: Ensure that your OpenAI API key is set as environment variable OPENAI_API_KEY.
• Gemini: Ensure that your VertexAI setup is correct in case you wanna benchmark gemini-pro too.
• Mistral: Setup the MISTRAL_API_KEY env variable as well as MISTRAL_ENDPOINT (Azure)
• Anthropic: Setup the ANTHROPIC_API_KEY env variable

3. Download all datasets - the download link is provided when executing the benchmark script for the first time. Please note that the datasets folder should be on the same level as the repository folder.
4. Execute the corresponding benchmark script. For example:

    python3 src/benchmark_doc_vqa.py "gpt-3.5-turbo-16k" "simple"

The following models are benchmarked:

• gpt-3.5-turbo-16k (Version gpt-3.5-turbo-16k-0613 )
• gpt-4-turbo (Version gpt-4-1106-preview)
• gemini-pro (Version 1.0)
• mistral-large (Version 03/2024)
• claude-3 (Version claude-3-opus-20240229)
• gpt-4-vision-preview (Version gpt-4-1106-vision-preview )

The following prompt methods are supported:

• simple - Simple text concatenation after OCR with GooleOCR
• latin - Method as introduced by Wang et al.
• sft - DeepOpinion internal only
• vision - If images should directly be used. Requires a model with vision capabilities e.g. gpt-4-vision

5. The final ANLS* is shown on the console.

How to Execute all Unit Tests

To run all unit tests simply execute pytest

Packaging

See https://packaging.python.org/en/latest/tutorials/packaging-projects/