Last updated on January 26, 2025
Every time the Large language Models steals the spotlight. The Small Language Models are unsung heroes of AI. The SMLs are very important not less than small LLMs. The small LLMs are for specific tasks. Due to their specificity and uniqueness, they require less resources. Hence they can be used in devices such as mobile phones also. This makes them highly valuable.
Introducing Small Language Models (Small LLMs)
These are the down-scaled version of their large counterparts. Due to the less problem surface area, they don’t require massive resources such as hardware or datasets or models. These are very light weight which make them suitable to work in any kind of environment where no large dataset is required.
Photo by Steve Johnson on Unsplash
Key features of Small LLMs
Efficiency: It requires less computational power and memory
Task Specific: They are task based and the surface area of computation is small. They are often used in chatbots, translators etc
Easy to deploy: Easier to train and deploy due to their small nature
The technology behind Small LLMs
They also use neural network similar to LLMs but very fewer and specific parameters. It comprises of faster processing and lower resource utilisation. The knowledge distillation and pruning technique is used for designing these Small LLMs.
Training Small LLMs
It involves training with small but specific data sets. Despite their small size they have high accuracy for specific tasks. The focus is on optimising performance for specific applications rather than general language understanding.
Application of Small LLMs
- Mobile Applications: Running efficiently on smart phones and portable devices
- Customer Services: Empowers chatbots and virtual assistants
- Education Tools: Assisting students in learning new concepts
- Translation Services: Realtime translations
Ethical Considerations
As we know it uses datasets for process and computation. It is a big concern that the data is used properly and privacy is not compromised. The developers should appropriately curate the data and use test elements for avoiding misuse of data.
Challenges and Limitations
Limited Generalisation: They are task specific, hence they cannot perform better on different problems
Resource Constraints: Small size makes them not worthy for large problem solving.
Scalability: The scalability is limited as it is trained in the very narrow datasets.
Addressing Limitations
The scientists and researches are continuously making effort to increase the usage sphere. The trial on different algorithms and datasets is being performed. We might come across some good scalability on small LLMs.
Innovations and Research in Small LLMs
1.Efficient Model Architectures: Developing neural network architecture which require less data and generate more accuracy.
2. Transfer Learning: Reduction on the need of extensive data and training on existing pre tested data, basically Leveraging pre-trained models.
3. Model Compression: Quantisation and pruning methods are used for model compression
4. Low-Resource Training: Exploring methods to train Small LLMs on limited data and computational power
Small Vs Large LLMs
While both have their unique characteristics. The large LLMs require huge data sets and power. The hardware required for these LLMs are quite expensive. The purpose of using large is huge such as trained on trillions of token.
While the small are very narrow such translator on mobile device. The problem surface area is small. Hence it requires less power, dataset and can run mobile device also without draining battery.