Domain-Specific LLMs: Powering Contextual Responses for Better Customer Experience
The blog encapsulates
There was a time when Google was the undisputed source for every answer we sought. Now, ChatGPT delivers detailed, human-like responses, impressing users with its depth and ease of information retrieval. The magic of large language models (LLMs) has transformed how we interact with knowledge, offering insights in seconds and revolutionizing information retrieval.
We’re in the era of personalization and customization, and LLMs are evolving in parallel to meet the unique demands of various industries, —ushering in the next frontier: domain-specific LLMs.
This blog explores why domain-specific LLMs are essential today, the techniques behind fine-tuned data models, and real-world applications of domain-specific LLMs.
What is a Domain-Specific LLM?
While LLMs can interact naturally, it’s important to understand that they are powered by neural networks trained to predict language patterns. They can’t tell what is contextually true or link words to real-world things like humans do. Also, some LLMs need help with handling longer text. An LLM requires a sufficiently large context window to generate relevant and coherent output.
This is where industry-specific LLMs make a difference. By leveraging smart search tools, they provide more relevant, accurate answers tailored to organizations’ specific needs.
Let’s get a glimpse of domain-specific LLM using this image:
Figure 1: Domain-specific LLM model outperforms Base Model
The results show a prominent difference, better context, and more accurate results. We have been searching for these features in our existing LLM models.
To emphasize further, according to Gartner1 by 2027, over 50% of GenAI models used by enterprises will be tailored to specific industries or business functions, a sharp increase from just 1% in 2023.
The message is loud and clear: domain-specific LLMs are the next big thing.
How Domain-Specific LLMs Outshine?
Having established what domain-specific LLMs are, let’s delve into how they outperform general-purpose models.
- More Precise Outputs: Models trained on narrow, domain-specific data provide more accurate and contextually relevant outputs, reducing the risk of misunderstandings or inaccuracies.
- Better Data Security: Deploying your custom domain-specific models in a private cloud can address LLMs and strengthen the protection of sensitive, business-critical data.
- Consistency in Outputs: Specialized models are more consistent in their responses, providing reliable information aligned with industry standards and practices.
- Personalized Customer Experience: Domain-specific LLMs boost customer engagement by delivering tailored, precise information, leading to more personalized services and increased satisfaction.
Training a Domain-specific LLM
Specialized LLMs can be trained from scratch or fine-tuned, with fine-tuning often being more efficient.
Foundational Model
Creating domain-specific LLMs from scratch requires training the model on vast amounts of industry-specific text. The model learns by predicting missing words in sentences, gradually developing an understanding of the language unique to that field. After training, engineers fine-tune the model to ensure accuracy, resulting in a highly specialized tool tailored to the industry’s needs. A notable example is BloombergGPT, which was built exclusively for the financial industry. Trained on decades of financial data, it took 53 days and 50 billion parameters using NVIDIA GPUs on AWS to create this powerful model.
Fine-Tuning of Models
While training models from scratch can deliver exceptional results, the time and resources involved make it impractical for many organizations. Fine-tuning the pre-trained models like GPT or Llama offers a more efficient alternative. This approach enhances the model with new, domain-specific knowledge using smaller datasets, understanding the jargon in your industry and knowledge from a specific genre while maintaining its original strengths. Fine-tuning techniques like Transfer Learning, Adapter Layers, and Retrieval-Augmented Generation (RAG) have proven highly effective, with RAG emerging as the preferred method. Let’s explore RAG in detail and highlight its key advantages.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) combines the strengths of pre-trained models with information retrieval systems. By searching external sources, such as databases and documents, RAG can provide more accurate and relevant answers. It identifies the most pertinent data, integrates it with its existing knowledge, and generates precise and up-to-date responses. The results are exceptionally promising, with RAG projected to achieve an impressive CAGR of 44.7% from 2024 to 20302.
Let’s dive into the details of how RAG works with the diagram below:
Figure 2: RAG’s Components and Functioning
It broadly encapsulated two procedures: Retrieval and Generation. Retrieval is the core, and it extracts data from external sources.
Retrieval
- RAGs utilize advanced search algorithms to fetch data from external sources like web pages, knowledge bases, and databases.
- The retrieved data is then pre-processed, which involves tasks such as tokenization, stemming, and removing stop words.
Generation
- The pre-processed data is integrated into the pre-trained LLM.
- This integration enhances the LLM’s context, giving it a deeper understanding of the subject.
- With this improved context, the LLM can generate more accurate, detailed, and engaging responses.
The reasons why RAG is the most preferred fine-tuning method are:
- Seamless access to external knowledge sources
- Solid factual foundation for more accurate outputs.
- Maintains strong contextual relevance.
- Ensures consistent accuracy, RAG enhances the quality of responses.
- Leverages vector databases to enable more precise retrieval based on the semantic meaning of the data.
Proven and Available: Solutions Already Making Waves
The market is already gearing up with domain-specific LLMs; healthcare and banking have been the pioneers. We just saw the example of BloombergGPT. Here are a few more established examples.
- Med-PaLM 2: A specialized language model by Google, trained on biomedical literature, that can accurately answer medical questions.
- KAI-GPT: This model is designed for conversational AI in banking, offering transparent and secure interactions for banking customers.
- BioBERT: A domain-specific model based on BERT, fine-tuned on extensive biomedical texts for specialized language representation in the biomedical field.
- ClimateBERT: A BERT-based model trained on over 2 million paragraphs of climate-related texts, including news and research, tailored for climate-specific language tasks.
It is a good example of fine-tuning models for different domains.
Fine Tuned LLM for Better Customer Experience
The latest LLM advancements are transforming how industries like healthcare, finance, and environmental science harness artificial intelligence. Fine-tuned models offer a simpler path for organizations to adopt domain-specific LLMs, delivering precise, contextually relevant responses. With RAG leading the charge, these models promise to revolutionize AI, offering faster, more accurate customer service and setting new benchmarks for efficiency and innovation.
At V2Solutions, we’ve successfully implemented customized LLM solutions for our clients and delivered high-quality responses, optimizing their productivity and efficiency. Leverage our expertise to adopt the best-fitting LLM solutions to boost your customer experience with precision and efficiency.
Get an LLM solution tailored to your industry and make a greater impact. Connect with us today!