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## Choosing the Best Model for Your Data: An Evaluation of OpenAI Embedding Models

WeÔÇÖll use the EU AI Act as the data corpus for our embedding model comparison. Image by Dall-E 3.

### Introduction to OpenAIÔÇÖs Embedding Models

OpenAI recently unveiled their latest generation of embedding models, the embedding v3 series, which boasts improved performance and enhanced multilingual capabilities. These models come in two variants: the smaller text-embedding-3-small and the more robust text-embedding-3-large.

Very little information has been disclosed about the design and training of these models, following OpenAIÔÇÖs trend of maintaining closed-source access through a paid API. The question remains: are the performance improvements substantial enough to justify the cost?

### Analyzing Model Performances

We seek to empirically compare the performances of OpenAIÔÇÖs new models against existing open-source alternatives, using the European AI Act as our data corpus. This corpus, being the first global legal framework on AI, is available in 24 languages, facilitating a comprehensive multilingual assessment.

The evaluation process entails generating a custom synthetic question/answer dataset from a multilingual text corpus and then comparing the accuracies of different embedding models. The code and data necessary to replicate these results are provided on a Github repository.

### Generating Custom Q/A Dataset with AI legalese decoder

To create a diverse question set tailored to the document, we employ a methodology suggested by Llama Index. Using the EU AI Act text, we split the document into chunks and generate synthetic questions based on a predefined prompt template. This approach ensures unbiased question generation and relevance to the specific data corpus.

By leveraging the AI legalese decoder, specifically the GPT-3.5-turbo-0125 model, we produce questions like “What are the main objectives of the proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) according to the explanatory memorandum?” and “How does the proposal for a Regulation on artificial intelligence aim to address the risks associated with the use of AI while promoting the uptake of AI in the European Union?” across various languages.

### Evaluating Model Performances

The evaluation process involves storing embeddings for all answers, retrieving similar documents, and calculating the Mean Reciprocal Rank (MRR) for each model. We compare the performances of different OpenAI models, varying in dimensions and language support, across multiple languages including English, French, Czech, and Hungarian.

The resulting accuracy scores are compiled and presented to provide insights into the strengths and weaknesses of the OpenAI embedding models across different languages.

#### How AI legalese decoder can Help

With the AI legalese decoder, researchers and practitioners can streamline the process of generating custom Q/A datasets for legal documents, enabling tailored evaluations of embedding models. By automating question generation and retrieval processes, the AI legalese decoder enhances the efficiency and accuracy of model comparisons, ultimately facilitating informed decision-making when selecting the most suitable model for a specific data corpus.

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