Overview of Small Language Models (SLMs) in AI
By 2025, Small Language Models (SLMs) are anticipated to significantly influence the field of artificial intelligence. SLMs are poised to drive the democratization and business impact of AI due to their unique advantages.
Characteristics and Advantages of SLMs
- Smaller Datasets: SLMs operate using smaller datasets, making them highly effective for organizations focusing on internal data analysis.
- Cost-Effective and Efficient: They require less computational power, are easier to train, and are more cost-effective compared to Large Language Models (LLMs).
- Specific Task Suitability: SLMs are better suited for specific, often critical tasks, allowing for deeper impact within organizations.
SLMs in 2024 and 2025
Throughout 2024, SLMs achieved significant adoption due to their suitability for mass usage and democratization of AI. They will gain prominence in 2025, especially in sectors requiring high accuracy and privacy, like healthcare and finance.
Use Cases for SLMs
- Healthcare:
- Enhancing patient interaction and support.
- Transcribing patient interactions and data entry for electronic health records.
- Providing preliminary diagnostic support.
- Banking and Finance:
- Offering personalized financial advice.
- Enhancing fraud detection systems.
- Improving document analysis and processing.
Future Developments and Impact
- SLMs are expected to integrate into business processes and run on edge devices, allowing user-controlled data exchanges.
- They will be pivotal for real-time GenAI applications on mobile, IoT, and edge devices.
- Open-source initiatives will continue to aid in fine-tuning SLMs using LLMs, enhancing their capabilities.
