Let’s start by defining first LLMs.
What are LLMs?
LLMs (Large Language Models), are advanced AI systems capable of understanding and generating various forms of content, including text, code, images, video, and audio. These models are trained on at least one billion parameters (data points), which allow them to grasp language patterns and respond appropriately.
LLMs find applications in natural language processing tasks such as text generation, translation, sentiment analysis, data analysis, question answering, and text summarisation.
Evolution of LLMs
Key milestones include:
- 1966 ELIZA: The first chatbot simulating a psychotherapist.
- 2013 word2vec: Efficient methods for learning word embeddings from raw text.
- 2018 GPT and BERT: Groundbreaking models.
- 2020 GPT-3: A significant leap.
- Late 2021 and 2022: GPT-4 and other advancements.
- Statistical models: Developed to learn patterns from text data.
LLMs vs. NLP
While NLP (Natural Language Processing) models interpret or transform existing text, LLMs excel at generating new, coherent text from scratch.
They can create essays, stories, and even computer code that mimics human writing styles.
Light LLMs
Nowadays, though, there is an increasing importance of smaller models (light LLMs) for specific domain applications.
While the largest models would all be “general purpose”, light LLMs are developed with a specific sector use in mind.
That is:
- Large models use a huge number of parameters, without tuning to a specific use, use a lot of energy, sometimes with questionable reliability, and that provide answers even when they don’t know them.
- Smaller models consider the use that is going to be given to it, refining its responses (fine-tuning) the specific model for a specific use.
Light LLMs benefits
- Efficiency: Light LLMs require fewer computational resources, making them faster and more cost-effective.
- Scalability: Companies can deploy light LLMs across various applications without straining infrastructure.
- Customisation: Light models allow fine-tuning for specific tasks, tailoring them to company needs.
- Privacy: Smaller models reduce the risk of inadvertently leaking sensitive information.
- Easier Maintenance: Light LLMs are simpler to manage and update.
To conclude, while both open-source and closed LLMs have their merits, light LLMs offer practical advantages for companies seeking efficient, adaptable solutions. Therefore, you should consider your specific requirements when choosing the right LLM for your organisation.
→ Check our LLMs solution