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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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  1. Home
  3. Tiny Language Models For Automation And Control: Overview, Potential Applications, And Future Research Directions.
  1. Home
  3. Tiny Language Models For Automation And Control: Overview, Potential Applications, And Future Research Directions.

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Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions.

Ismail Lamaakal1, Yassine Maleh2, Khalid El Makkaoui1

  • 1Multidisciplinary Faculty of Nador, Mohammed Premier University, Oujda 60000, Morocco.

Sensors (Basel, Switzerland)
|March 17, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Tiny Language Models (TLMs) provide efficient alternatives to Large Language Models (LLMs) for resource-constrained devices. This survey explores TLM techniques, applications in edge computing and IoT, and future research directions for optimized performance.

Keywords:
IoTedge computinglarge language modelsmodel compressionoptimizationsmall language modelstiny language models

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Natural Language Processing

Background:

  • Large Language Models (LLMs) achieve high performance in Natural Language Processing (NLP) but are computationally intensive.
  • Resource-constrained devices require efficient, low-power models for applications like smartphones and IoT.

Purpose of the Study:

  • To provide a comprehensive survey of Tiny Language Models (TLMs), also known as BabyLMs.
  • To explore TLM architectures, methodologies, and their applications in edge computing, IoT, industrial automation, and healthcare.

Main Methods:

  • Survey of TLM architectures and methodologies.
  • Analysis of compression techniques including knowledge distillation, quantization, and pruning.
  • Exploration of TLM applications and challenges.

Main Results:

  • TLMs offer compact alternatives to LLMs for efficient deployment on edge devices.
  • Key techniques like knowledge distillation, quantization, and pruning enable TLM effectiveness.
  • Emerging applications span automation, control, IoT, and healthcare.

Conclusions:

  • TLMs are crucial for bringing advanced NLP capabilities to resource-limited environments.
  • Addressing challenges like accuracy trade-offs and generalization is key for TLM advancement.
  • Future research should focus on hybrid compression and hardware-specific optimizations.