ETMI5: Explain to Me in 5

In this section, we delve into the limitations of general AI models in specialized domains, underscoring the significance of domain-adapted LLMs. We explore the advantages of these models, including depth, precision, improved user experiences, and addressing privacy concerns.

We introduce three types of domain adaptation methods: Domain-Specific Pre-Training, Domain-Specific Fine-Tuning, and Retrieval Augmented Generation (RAG). Each method is outlined, providing details on types, training durations, and quick summaries. We then explain each of these methods in further detail with real-world examples. In the end, we provide an overview of when RAG should be used as opposed to model updating methods.

Using LLMs Effectively

While general AI models such as ChatGPT demonstrate impressive text generation abilities across various subjects, they may lack the depth and nuanced understanding required for specific domains. Additionally, these models are more prone to generating inaccurate or contextually inappropriate content, referred to as hallucinations. For instance, in healthcare, specific terms like "electronic health record interoperability" or "patient-centered medical home" hold significant importance, but a generic language model may struggle to fully comprehend their relevance due to a lack of specific training on healthcare data. This is where task-specific and domain-specific LLMs play a crucial role. These models need to possess specialized knowledge of industry-specific terminology and practices to ensure accurate interpretation of domain-specific concepts. Throughout the remainder of this course, we will refer to these specialized LLMs as domain-specific LLMs, a commonly used term for such models.

Here are some benefits of using domain-specific LLMs:

1. Depth and Precision: General LLMs, while proficient in generating text across diverse topics, may lack the depth and nuance required for specialized domains. Domain-specific LLMs are tailored to understand and interpret industry-specific terminology, ensuring precision in comprehension.
2. Overcoming Limitations: General LLMs have limitations, including potential inaccuracies, lack of context, and susceptibility to hallucinations. In domains like finance or medicine, where specific terminology is crucial, domain-specific LLMs excel in providing accurate and contextually relevant information.
3. Enhanced User Experiences: Domain-specific LLMs contribute to enhanced user experiences by offering tailored and personalized responses. In applications such as customer service chatbots or dynamic AI agents, these models leverage specialized knowledge to provide more accurate and insightful information.
4. Improved Efficiency and Productivity: Businesses can benefit from the improved efficiency of domain-specific LLMs. By automating tasks, generating content aligned with industry-specific terminology, and streamlining operations, these models free up human resources for higher-level tasks, ultimately boosting productivity.
5. Addressing Privacy Concerns: In industries dealing with sensitive data, such as healthcare, using general LLMs may pose privacy challenges. Domain-specific LLMs can provide a closed framework, ensuring the protection of confidential data and adherence to privacy agreements.

If you recall from the previous section, we had multiple ways to use LLMs in specific use cases, namely

1. Zero-shot learning
2. Few-shot learning
3. Domain Adaptation

Zero-shot learning and few-shot learning involve instructing the general model either through examples or by prompting it with specific questions of interest. Another concept introduced is domain adaptation, which will be the primary focus in this section. More details about the first two methods will be explored when we delve into the topic of prompting.

Types of Domain Adaptation Methods

There are several methods to incorporate domain-specific knowledge into LLMs, each with its own advantages and limitations. Here are three classes of approaches:

1. Domain-Specific Pre-Training:

   • Training Duration: Days to weeks to months
   • Summary: Requires a large amount of domain training data; can customize model architecture, size, tokenizer, etc.

   In this method, LLMs are pre-trained on extensive datasets representing various natural language use cases. For instance, models like PaLM 540B, GPT-3, and LLaMA 2 have been pre-trained on datasets with sizes ranging from 499 billion to 2 trillion tokens. Examples of domain-specific pre-training include models like ESMFold, ProGen2 for protein sequences, Galactica for science, BloombergGPT for finance, and StarCoder for code. These models outperform generalist models within their domains but still face limitations in terms of accuracy and potential hallucinations.