Large Language Models (LLMs), like GPT, PaLM, LLaMA, etc., have attracted much interest because of their incredible capabilities. Their ability to utilize the strength of Natural Language Processing, Generation, and Understanding by generating content, answering questions, summarizing text, and so on have made LLMs the talk of the town in the last few months.
However, the high expenses of training and maintaining big models, as well as the difficulties in customizing them for particular purposes, come as a challenge for them. Models like OpenAI’s ChatGPT and Google Bard require enormous volumes of resources, including a lot of training data, substantial amounts of storage, intricate, deep learning frameworks, and enormous amounts of electricity.
What are Small Language Models?
As an alternative, Small Language Models (SLMs) have started stepping in and have become more potent and adaptable. Small Language Models, which are compact generative AI models, are distinguished by their small neural network size, number of parameters, and volume of training data. SLMs require less memory and processing power than Large Language Models, which makes them perfect for on-premises and on-device deployments.
SLMs are a viable option in situations where resource constraints are a factor because the term ‘small’ refers to both the model’s efficiency and architecture. Because of their lightweight design, SLMs provide a flexible solution for a range of applications by balancing performance and resource usage.
Significance of Small Language Models
- Efficient: When it comes to training and deploying, SLMs are more efficient than Large Language Models. Businesses looking to minimize their computing costs can operate on less powerful gear and require less data for training, which can save a significant amount of money.
- Transparency: Compared to sophisticated LLMs, smaller language models typically display more transparent and explicable behavior. Because of its transparency, the model’s decision-making processes are easier to comprehend and audit, making it easier to spot and fix security flaws.
- Accuracy: SLMs produce factually correct information and are less prone to display biases because of their smaller scale. They can consistently produce correct findings by undergoing targeted training on particular datasets, which comply with the standards of different businesses.
- Security: When it comes to security, SLMs have better features than their larger counterparts. SLMs are intrinsically more secure because they have smaller codebases and fewer parameters, which decreases the possible attack surface for bad actors. Control over training data helps to strengthen security further by enabling businesses to select relevant datasets and reduce the risks associated with malicious or biased data.
Examples of Small Language Models
- DistilBERT is a quicker, more compact version of BERT that transforms NLP by preserving performance without sacrificing efficiency.
- Microsoft’s Orca 2 uses synthetic data to refine Meta’s Llama 2 and achieves competitive performance levels, particularly in zero-shot reasoning tasks.
- Microsoft Phi 2 is a transformer-based Small Language Model that places an emphasis on adaptability and efficiency. It displays amazing abilities in logical reasoning, common sense, mathematical reasoning, and language comprehension.
- Modified iterations of Google’s BERT model, including BERT Mini, Small, Medium, and Tiny, have been designed to accommodate varying resource limitations. These versions offer flexibility in terms of applications, ranging from Mini with 4.4 million parameters to Medium with 41 million.
Practical Applications of Small Language Models
- Automation of Customer Service: SLMs are ideally suited for automating customer service jobs due to their increased agility and efficiency. Micro-models can efficiently handle routine problems and consumer inquiries, freeing up human agents to concentrate on more individualized interactions.
- Product Development Support: By helping with idea ideation, feature testing, and customer demand prediction, edge models are essential to product development.
- Email Automation: SLMs help to expedite email correspondence by composing emails, automating responses, and making suggestions for enhancements. Guaranteeing prompt and efficient email exchanges increases productivity for both individuals and companies.
- Sales and Marketing Optimisation: Personalised marketing material, including product suggestions and customized email campaigns, is best produced by small language models. This gives companies the ability to maximize their marketing and sales efforts and send more precise and impactful messages.
In conclusion, Small Language Models are becoming incredibly useful tools in the Artificial Intelligence community. Their versatility in business environments, along with their efficiency, customizability, and improved security features, place them in a strong position to influence the direction AI applications take in the future.
Tanya Malhotra is a final year undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and critical thinking, along with an ardent interest in acquiring new skills, leading groups, and managing work in an organized manner.