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The Rise of Small Language Models: The Next Big Shift in AI

Artificial intelligence has been a game-changer in reshaping industries and daily life, and the horizon for 2025 paints an exciting picture of transformation powered by Small Language Models (SLMs). While the past decade spotlighted the potential of Large Language Models (LLMs), the growing buzz around their smaller counterparts signals a seismic shift in how businesses and developers leverage AI for more precise, cost-effective, and tailored solutions.

Small Language Models in AI


What Are Small Language Models?

SLMs are compact, efficient versions of LLMs, capable of generating human-like text while being trained on smaller datasets. Unlike their larger counterparts, they are:

  • Easier to train and deploy.
  • Less resource-intensive, consuming significantly less computational power.
  • More cost-effective for specific, domain-focused applications.

By working with smaller, often proprietary datasets, SLMs offer businesses a more secure and customisable approach to integrating AI into their operations.

Why SLMs Are Gaining Ground

The growing adoption of SLMs is underpinned by their practicality and relevance. In 2024, a wave of lightweight models, like Microsoft's Phi and Google’s Gemma, gained traction, demonstrating the feasibility of AI on a smaller, smarter scale. These models are not only highly accurate but also offer low hallucinatory tendencies, making them ideal for critical sectors where precision is paramount.

Privacy-sensitive industries like healthcare and finance are at the forefront of this transition. Here’s why:

Healthcare:

  • Streamlining electronic health records and transcribing patient interactions.
  • Supporting preliminary diagnostics, particularly in underserved regions.

Finance:

  • Enabling personalised financial advice.
  • Enhancing fraud detection and automating document processing.

Real-World Applications of SLMs

SLMs are paving the way for AI-powered tools that can operate on edge devices, making them accessible and deployable in real-time, resource-constrained environments. The following industries are set to reap substantial benefits:

  • Customer Support: Revolutionising digital agents with unparalleled personalisation, empathy, and support in multiple languages.
  • Legal and Manufacturing: Automating complex workflows and improving operational efficiencies with domain-specific insights.
  • IoT and Mobile Devices: Powering real-time generative AI for personalised user experiences on limited-resource devices.

The Role of Open-Source and Fine-Tuning

The open-source revolution of 2024 has provided developers with robust frameworks to fine-tune SLMs, often leveraging LLMs for guidance. This has enabled the creation of tailored solutions that can seamlessly integrate into business processes, granting companies more control over their data and AI’s impact on operations.

As these smaller models continue to evolve, their adoption is poised to make AI truly democratic, bringing its transformative capabilities to smaller organisations, niche applications, and everyday users.

Shaping the Future - SLMs in Education

Chandigarh University has long been at the forefront of embracing innovation, and AI-augmented education is a testament to this commitment. The university’s advanced curriculum, coupled with state-of-the-art technology and infrastructure, equips students to lead in this transformative era. By integrating AI-powered tools like SLMs into academic and administrative processes, CU is not just educating students but preparing them to shape the future of industries worldwide.

As we step into a world where smaller models drive bigger changes, Chandigarh University stands as a beacon of progress, ready to redefine what's possible in technology and learning.

Also read: How AI is transforming Education

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