Comparing Open Source and Proprietary Language Models for E-Commerce Solutions

Updated 7 September 2023

The choice between open-source and Proprietary Language models depends on various factors, which include your specific need, budget, and availability of resources like a tech team, required data, etc.

Open Source LM:

  1. Free to Use: Open source models are free to use which can be of significant advantage for businesses with budget constraints, especially smaller e-commerce startups.

    Here, we are using a pre-trained “Xenova/distilbert-base-uncased-distilled-squad” Language Model for ‘question-answering’.  To use this you just need to install transformers by running ‘npm install transformers’.
  2. Customization: You have more control over open-source models and they can be customized as per the requirements. You can also fine-tune open-source models over your data and adapt it to your use case.
  3. Transparency: Open source models are more transparent in terms of how they work and what data they are trained on, which can be important for compliance and preparing the data for finetuning.
  4. Community and Flexibility: Open-source models often have an active community of developers and researchers. This means you can benefit from continuous improvements, updates, and a wide range of available resources and extensions.

Considering Open Source LM for E-commerce:

  1. Expertise required: To effectively use and customize open-source LLMs, you may need a team with expertise in Natural Language Processing and Machine Learning.
  2. Resource Intensive: Training and fine-tuning open-source models will require proper data and computational power.
  3. Training Required: The above model is trained to answer the question from a given set of documents, but it will just provide the raw answer rather than presenting it in a nice manner as it should be for e-commerce use. So, you need to fine-tune this to answer in a specific manner. To use it in our Chatbot Module, you need to train it for conversations.

Proprietary LLM:

  1. Easy to use: Proprietary LLMs are often designed to be user-friendly and come with a dedicated support team, making them easier to use.

    This is using OpenAI API to answer the user’s query from the given set of documents. This can be used in e-commerce as a chatbot for answering the user’s query regarding products, return policy, shipment policy, etc.
  2. Performance: Some Proprietary LLMs are fine-tuned for specific tasks like user-friendly conversation, which proves to be very useful for e-commerce applications.
  3. Giving Conversational Answers without any specific training: You are not required to separately train this model to answer the user’s query in a professional manner. Because of its impressive responses and ease of use, we at Bagisto have used the OpenAI LLM in our Chatbot Module.

Considerations for Proprietary LLMs:

  1. Cost: Proprietary LLMs typically come with subscription/licensing fees, which can be a significant ongoing cost.
  2. Customization Limitations: Proprietary LLMs may offer less flexibility for customization compared to open-source models.

In summary, the choice between open-source and proprietary LLMs depends on your specific  circumstances:

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