2024/02/19 | Written By: Sungmin Park (Content Manager)

The emergence of large language models is opening up new opportunities and pushing the limits of existing services across the industry. This blog explores the practical applications of domain-specific LLMs and how they bring transformative changes across industries.

What is Domain-Specific LLM,
and how does it differ from other language models?

When companies adopt external AI technology, they often face two obstacles; data security issues and AI hallucinations. These issues were caused by the features of generative AI, which may produce inaccurate responses and cause data leakage. For these reasons, Domain-specific LLMs are emerging as an alternative option due to their ability to go beyond the limitations of generalist LLMs. The strength of these models is that can understand specialized vocabulary or context while also ensuring improved performance. A Domain-Specific Large Language Model is trained for a particular domain or industry, so it can providing more accurate and contextually relevant responses and preventing AI hallucination in specialized fields. How does a it differ from other language models?

Training a Domain-Specific LLM involves a combination of pre-training and fine-tuning on a targeted dataset to perform well-defined tasks in specific domain. This approach differs from traditional language model training, which typically involves pre-training on a large and diverse dataset to perform a various tasks and language patterns. Domain-specific models are trained on large amounts of text data that are specific to a particular domain to perform a deep understanding of the linguistic nuances within it. This boosts LLMs to communicate effectively with specialized vocabulary and provide high-quality answers. For this reason, maintaining industry terminologies and staying updated on industry issues are quite important for leveraging it.

Real-world examples of Domain-Specific LLMs

By now you are probably curious: what can I do with a domain-specific LLM and how does it changing the work reality? Let's explore some real-world examples.

1. Law

• Support Legal analytics

Imagine an LLM that supports legal professionals with real-time analytics of legal documents. SaaS platform 'Predictice' is a legal research and search engine that delivers legal information. It was released by a French legal technology company, using artificial intelligence to analyze and organize 25 million pieces of legal data. With features such as precedent search, the latest legal updates, and legal news, it helps lawyers and legal professionals work more efficiently and make better decisions. Predictice uses ChatGPT to summarize court decisions for users. Their documentary collection includes all legally accessible court decisions and is updated 24 hours a day.

• Tax

Domain-specific LLMs are also being applied to tax and legal domains. They can contribute to improving the efficiency of legal services and identifying inconsistencies in legal tasks. ‘Blue J’ is a generative AI designed for tax experts to perform exceptional analysis and deliver more insights faster. Tax experts can leverage AI to predict the outcome of the scenario by analyzing thousands of previous decisions instantly. Pre-built summaries on tax topics with statute references, relevant cases, and more supports research effortless.

2. Math

• MathGPT (Upstage-Qanda-KT)

Math-specific large language model is becoming a global math solver. Mathematics is often considered one of the most challenging subjects, as even the majority of models are hard to perform tasks perfectly. Why is it so difficult? The reason is that solving math problems requires complex reasoning skills, such as clear logic and sequential thinking. However, language models are primarily adept at linguistic skills for understanding and creating written text in context. This makes it difficult to create math-specific LLMs.

Despite all that, Upstage-Qanda 13B model, nicknamed “MathGPT”, has achieved SoTA performance over MS ToRA 13B on both GSM8K and MATH benchmark datasets in January. To give you an idea, GSM8K consists of 8.5K high quality grade school math problems and MATH includes 12,500 challenging competition mathematics problems in a dataset.

Notably, MathGPT has surpassed ChatGPT's average performance across various benchmark tests and even outperformed GPT-4 on MATH with an impressive accuracy of 48.8 percent, showcasing its competitiveness with industry-leading models. It’s expected that MathGPT will be able to understand complex mathematical formulas and concepts, providing accurate solutions for users.

3. Healthcare

• A clinical specialized large language model

Can you imagine if it's difficult to tell whether a patient case was written by a doctor or AI? This technology is already a reality. Healthcare-specific LLMs are transforming the industry by assisting healthcare professionals make informed decisions and process large amounts of medical text. It can be useful for patients to get the right information regarding their wellness.

A clinical generative large language model, 'GatorTronGPT' was developed by researchers at the University of Florida and NVIDIA. It uses biomedical natural language processing to generate doctors' notes. This is an example of how the healthcare industry is being transformed by the application of artificial intelligence in clinical work.

• Biomedical text generation and mining

BioGPT is a domain-specific generative pre-trained transformer language model designed for biomedical text generation and mining. It’s pre-trained on 15M PubMed abstracts from scratch, so the model better understands specialized vocabulary. This model can be used as a helpful question-answering system, assisting biomedical researchers in finding information within biomedical literature. Additionally, healthcare companies can leverage it for their researchers in drug discovery efforts.

4. Finance

• Improving financial task performance

In the realm of financial technology, NLP can greatly help in understanding unstructured data. A Financial-specific LLM is trained to perform finance-related tasks, such as classifying financial documents, recognizing entities in context, data augmentation, and more. It can revolutionize finance industry by providing advanced analytical capabilities, automating repetitive tasks, and enhancing decision-making.

BloombergGPT is the first finance-focused LLM that enhances Bloomberg's NLP applications, providing improved financial task performance and question-answering. Another feature of BloombergGPT is its ability to generate specialized query language and create proper content in the field.

• Finance Question-Answering

Kasisto is another real-world example of finance-specialized LLM. It is a conversational AI solutions for banking and finance, providing a means for financial institutions to achieve these objectives at a comfortable pace that is both a good fit for their brand aspirations and meets their employee and customer needs.

The purpose-built LLM for banking provides precise and reliable answers for banking use cases while safeguarding personal data security. It enables frontline bank employees to deliver exceptional customer care. Also, KAI Answers, powered by KAI-GPT, empowers bank employees to provide better customer service.

5. Commerce

• Tailored Generative AI Service for E-commerce Business

Domain-specific LLMs are also being applied in the commerce industry to accelerate innovation. By enhancing hyper-personalization, promoting customer interactions 24/7, optimizing the search experience, and streamlining various operations, companies can improve their business strengths.

A partnership between the e-commerce data platform company 'ConnectWave' and the AI company 'Upstage' is a real-world use case of it. Last September, they partnered to build a private, generative AI-powered large language model specifically for e-commerce businesses. By applying AI technologies, users can easily find the products they need and experience improved recommendations based on their interests. These features are expected to enhance consumer shopping satisfaction. Moreover, it will support hyper-personalization, automate shipping tracking and inquiries, provide summary reviews of shopping malls, and streamline content marketing.

Unleashing the potential of Domain-Specific LLMs

By exploring various use cases, we can discover the endless possibilities of domain-specific large language models, from automating simple tasks to processing complex ones. And as they continue to evolve, we can expect even more exciting advancements in entire industries. Ready to unlock your business potential? Try our powerful-purpose-trained SLM, ‘Solar Mini’, for exceptional performance that will help your business take flight!