‘Large language models’ (LLMs) is the latest buzzword in the field of artificial intelligence (AI). They have revolutionized AI by demonstrating remarkable proficiency in understanding and generating human-like text. These models, which are at the forefront of AI research, can be divided into two main categories, namely, general purpose LLMs and customizable LLMs. This blog delves into each type, their benefits, drawbacks, and applications. 

What Are General Purpose LLMs?

General purpose LLMs are a type of AI algorithm that employs deep learning techniques and extensive datasets to understand and generate human-like text. Because they are trained extensively on vast amounts of data, these LLMs learn patterns and language structures effectively. They have a vast knowledge repository because of the huge quantity of data they are trained on so general purpose LLMs are enabled to generate coherent text on a wide range of topics. Here is how it is used across industries. 

How Various Industries Use General purpose LLMs

1. Technology

• Chatbots: LLMs power chatbots and virtual assistants for customer support. 
• Text analysis: Data processing ability of general purpose LLMs assist in sentiment analysis, content recommendation, and text summarization. 

2. Finance

• Risk assessment: LLMs analyze financial data for risk assessment and fraud detection. 

• Market prediction: They are used for predicting stock market trends based on news and reports. 

3. Healthcare

• Medical records analysis: LLMs extract insights from electronic health records. 
• Drug discovery: They aid in analyzing scientific literature for drug discovery. 

4. E-commerce

• Product recommendations: LLMs recommend products based on user behavior and reviews. 
• Customer service: They provide automated responses to customer queries. 

5. Media and publishing

• Content generation: LLMs automate content creation for news articles and blogs. 
• Translation: They assist in translating content for global audiences. 

6. Legal

• Legal research: LLMs help lawyers search for relevant cases and legal documents. 

• Contract analysis: They assist in contract review and analysis. 

7. Marketing and Advertising

• Content marketing: LLMs generate marketing content and social media posts. 
• Targeted advertising: They analyze user data for targeted ad campaigns. 

8. Manufacturing

• Quality control: LLMs analyze quality control reports and identify issues. 
• Supply chain management: They assist in analyzing supply chain data and trends. 

9. Entertainment

• Scriptwriting: LLMs help generate scripts for movies and TV shows. 
• Content moderation: They assist in content moderation on social media and streaming platforms. 

10. Research and academia

• Scientific research: LLMs aid in scientific literature review and data analysis. 
• Automated surveys: They generate surveys and analyze responses. 

These are just a few examples of the diverse industries where general purpose LLMs find applications. Their versatility in understanding and generating human-like text makes them valuable tools across various sectors. Here are more reasons why they are so popular across industries and domains.  

Why General Purpose LLMs Are So Popular?  

General purpose LLMs are being used by individuals and businesses across the globe to streamline their day-to-day activities. Here is why their popularity shot up.  

1. Language understanding: General purpose LLMs are trained to handle queries in natural human language. They process and interpret a wide range of text data with high accuracy and make many jobs easier for humans.  
2. Pre-trained Knowledge: They are pre-trained on vast text corpora, which provides them with extensive general knowledge about various topics and domains, making them versatile in answering questions and generating text. 
3. Cost-Effective: Once trained, general purpose LLMs can be used for multiple applications. This makes them cost-effective over time as their initial training cost is spread across various use cases. 
4. Automation: They can automate text-related tasks such as content generation, translation, summarization, and text classification, saving time and effort. 
5. Data processing: General purpose can process huge volumes of data within seconds. This fast data processing speed enables them to be used in chatbots etc.   
6. While general purpose LLMs like ChatGPT, BERT, BARD, BING are being hailed for the content generation and data processing prowess, they have certain drawbacks which limit their usage. 

Drawbacks of General Purpose LLMs 

1. Speed and cost: Training and using general purpose LLMs can be computationally expensive and time-consuming due to their vast data requirements. Hence, they may not be convenient to use in many scenarios.  
2. Hallucinations: General purpose LLMs may generate incorrect or nonsensical information, which are referred to as hallucinations. Such hallucinations may lead to the spread of misinformation. 
3. Control: Human-produced data which these LLMs are trained on carry many specific biases. So, it is hard to control these biases from creeping into the text generated by LLMs. Thus, the output may perpetuate ethical biases or not meet community guidelines.  
4. Generalization: Since general purpose LLMs are trained on large data sets, they do not have expertise in any specific domain. Therefore, the information they provide about niche topics may be very generalized or inaccurate.  

The answer to these challenges is customizable LLMs. Read on to find out how customizable LLMs tackle these issues.  

What are Customizable LLMs?  

A customizable LLM is one that can undergo fine-tuning to suit specific tasks. This process of fine-tuning LLMs involves training them on fine-tuned domain-specific data, enabling them to generate text, understand context, and provide more precise and tailored responses for specialized applications or industries. Since these LLMs are trained on fine-tuned domain-specific data, they are enabled to provide more domain-specific and contextually aware responses. This is the primary reason for their popularity across industries. Here is a list of industries that employ customizable LLMs to their advantage.  

How Various Industries Use Customizable LLMs 

1. Healthcare 

Medical diagnosis: Customizable LLMs assist in diagnosing diseases based on patient records, medical literature, and symptom analysis. 

Drug discovery: They aid in drug discovery by analyzing chemical data and identifying potential compounds. 

2. Law 

Legal research: Customizable LLMs streamline legal research by summarizing cases, analyzing precedents, and providing legal opinions. 

Contract analysis: They review contracts, highlighting crucial terms, and ensuring compliance. 

3. Finance 

Algorithmic trading: Customizable LLMs analyze market data, helping traders make informed decisions. 

Risk assessment: They assess financial risks by analyzing economic indicators and market trends. 

4. Customer support 

Chatbots: Customizable LLMs power chatbots for handling customer inquiries, troubleshooting, and providing product information. 

Email responses: They draft personalized email responses for customer queries. 

5. E-commerce 

Product recommendations: Customizable LLMs offer personalized product recommendations based on user preferences and purchase history. 

Review analysis: They analyze customer reviews to extract insights and improve products. 

6. Media and Entertainment 

Content generation: Customizable LLMs create articles, scripts, and content for media outlets and entertainment platforms. 

Subtitle generation: They generate subtitles for videos, making content accessible. 

7. Education 

Personalized learning: Customizable LLMs offer personalized educational content and assessments tailored to students’ needs. 

Automated grading: They assist in grading assignments and providing feedback. 

8. Travel and Hospitality 

Travel recommendations: Customizable LLMs suggest personalized travel itineraries and accommodations. 

Translation services: They offer real-time translation services for tourists. 

9. Energy 

Energy efficiency: Customizable LLMs optimize energy usage in industrial processes and buildings. 

Predictive maintenance: They predict equipment failures, reducing downtime in energy production. 

10. Government 

Policy Analysis: Customizable LLMs analyze policy documents and legislative texts for policymakers. 

Public Services: They improve citizen services through chatbots and information dissemination. 

Why Customizable LLMs Are So Popular? 

The business world at large is waking to the up the wonders of customizable LLMs. Here is why.  

1. Domain-specific expertise: Customizable LLMs are tailored to specific domains or industries, making them more knowledgeable and capable of understanding context within that domain. 
2. Improved accuracy: Customization enhances the LLM’s accuracy for specialized tasks, as it’s fine-tuned on domain-specific data, resulting in more precise responses. 
3. Reduced bias: Fine-tuning allows developers to actively address and mitigate biases in the LLM, reducing the risk of generating biased or unfair content. 
4. Efficiency: customizable LLMs require less post-processing of generated text, as they are already optimized for specific applications, saving time and resources. 
5. Specialized use cases: Customizable LLMs excel in specific applications like medical diagnosis, legal document analysis, or industry-specific chatbots, addressing unique challenges. 

Multiple shortcomings that general purpose LLMs had are addressed by customizable LLMs. But they have certain drawbacks as well.  

Drawbacks of Customizable LLMs 

1. Data dependency: Customizable LLMs rely heavily on the quality and quantity of training data. Limited or biased data can result in suboptimal performance. 
2. Cost and resources: Developing and fine-tuning a customizable LLM can be resource-intensive, requiring significant computational power and expert knowledge. 
3. Bias amplification: If training data contains biases, customizable LLMs can inherit and amplify those biases, leading to potential ethical and fairness issues. 
4. Continuous maintenance: Customizable LLMs need regular updates and maintenance to stay relevant as language and data evolve. Hence, increasing operational costs. 
5. Limited multilingual support: Customizable LLMs may struggle with multilingual tasks if not specifically trained for multiple languages. 
6. Privacy concerns: Training Customizable LLMs with sensitive data may raise privacy and security concerns, especially in regulated industries. 
7. Lack of transferability: Customizable LLMs are often less transferable to different applications compared to more general LLMs. 

General Purpose LLMs and Customizable LLMs: A Partnership

The debate on the advantages and disadvantages of general purpose LLMs and customizable LLMs will keep evolving as they evolve over time, but what is most notable is that both have contributed to streamlining a multitude of process across various industries. At this point in time, be it an individual or a business, anyone can use an LLM to make work and life easier for themselves. What’s more is that they even have an option to choose the type of LLM that is best suited to their needs.  

At gnani.ai, we work with both kinds of LLMs to fit different enterprise needs across multiple use cases. To know more, contact us.  

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