Verticalizing AI21’s Jamba 1.5 with Bitext Synthetic Text
Efficiency and Benefits of Verticalizing LLMs – The Case of Jamba 1.5 Mini
The verticalization of Large Language Models (LLMs) is transforming the way businesses implement AI-based solutions. By adapting models like Jamba 1.5 Mini to specific sectors, organizations can obtain more precise, consistent, and contextually aligned responses tailored to the unique needs of their clients and industry. This approach demonstrates how a verticalized Jamba 1.5 Mini not only optimizes processes but also provides tangible value in terms of operational efficiency and personalization, enabling a more effective and profitable adoption of AI in specialized sectors.
Below, we highlight the key benefits of verticalizing this model, demonstrating its impact on reducing time, costs, and improving customer experience.
Advantages of Verticalizing LLMs
Cost Impact: Standard vs Verticalized Approach
Bitext’s verticalized model stands out for its two-step training approach, achieving significant savings in time and resources:
Cost Component | Generic Approach | Verticalized Model | Savings (%) |
Verticalization | High: Extensive fine-tuning | Low: Pre-finetuned vertical model | 60% |
Customization with company data | Medium: Additional fine-tuning required | Low: Specific customization needed | 30% |
Total training time | 3-6 months | 1-2 months | 50-60% |
Resource allocation | High: More data scientists and computational power | Low: Less intensive | 40-50% |
Operational disruption | High: Longer integration and testing phases | Low: Faster deployment | 50% |
Response Comparison: Standard Model vs Banking Verticalized Model
In this section we compare answers from the two models, Standard and Verticalized, to prove the advantages of verticalization. The use case for the comparison is Customer Support in Retail Banking. For that, we send one frequent query (“I want to open an account”) to both LLMs to test the adequacy of the answers. Also, and very important, we send the same query twice to each model to test consistency in answers.
Query #1: ‘I want to open an account’
Standard Model Response #1.1:
Sure, I can help you with that. Could you please specify what type of account you want to open? For example, a bank account, a social media account, a brokerage account, or something else? Additionally, providing more details about your requirements and preferences will help me offer the most relevant information.
The response requests the user to specify the type of account: bank, social media, etc. It makes sense, since the model has general knowledge and not vertical knowledge. As a result, it requires the customer to provide more details, prolonging the interaction.
Standard Model Response #1.2:
Sure, I can help you with that! To get started, I’ll need a bit more information about the type of account you’re looking to open. Here are a few common types of accounts and their specific requirements:
Full name
Date of birth
Address
Social Security Number (SSN) or Tax Identification Number (TIN)
Government-issued ID (e.g., driver’s license, passport)
Initial deposit (if required)
Full name
Address
Social Security Number (SSN)
Employment information
Investment experience
Initial deposit
Desired username
Password
Email address
Date of birth
Phone number (optional)
Full name
Address
Contact information
Social Security Number (SSN) or Tax Identification Number (TIN)
Proof of address (e.g., lease agreement, utility bill)
Please specify the type of account you want to open, and I’ll provide you with the detailed steps and requirements.
As in the previous response, the second one requires the user to specify the type of account. However, this time the length and tone of the answer is different, much longer and instruction oriented. The differences between these two answers to the same question makes clear that it’s hard for a general model to adhere to a particular corporate response style.
Verticalized Model Response (Jamba 1.5 Mini):
Verticalized Model Response #1.1:
I’ll do my best! I’m here to assist you with opening a bank account. It’s a simple process, and I’ll guide you through it step by step. Here’s what you need to do:
Congratulations! You’ve successfully opened an account with us. If you encounter any difficulties or have any questions along the way, feel free to reach out to our customer support team at {{Customer Support Phone Number}} or through the live chat on our website. We’re here to help you every step of the way. Welcome aboard!
This answer provides clear, specific instructions for opening a bank account, including links to the website and structured steps, ignoring the ambiguity of the term “account” and other potential meanings not relevant in banking sector. It reduces interaction time and improves the customer experience.
Verticalized Model Response #1.2:
I can facilitate the bank account opening process for you. Here’s what you need to do:
Should you encounter any difficulties or have further questions during the process, feel free to reach out. I’m here to assist you at every step.
This second answer is very similar in style, tone, length and structure to the previous answer, proving that verticalization can ensure consistency in answers to customers. This answer also outlines the complete process, anticipating possible questions and indicating how to contact support if needed. This delivers a seamless experience, demonstrating deep domain knowledge.
Summary of Key Results
The verticalized model better understands customer context (use case context), providing focused and relevant responses.
Conclusion
Verticalizing models like Jamba 1.5 Mini not only improves the quality and consistency of responses but also represents a more agile and cost-effective solution for businesses. By specializing in specific domains, verticalized LLMs offer a competitive advantage, enabling organizations to optimize processes, reduce costs, and enhance the customer experience with speed and precision.
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