Large Language Models (LLM)
LLMs
Large language models are complex language models, part of artificial intelligence, which use deep learning techniques through the use of neural networks. LLMs operate with very large datasets as well as a multitude of parameters (millions, billions, trillions, etc.).
Datasets within large language models contain immense quantities of unlabelled text. The large language models train on these datasets through self-supervised and semi-supervised learning, and as such, are able to perform comprehension, generation, and prediction of content. Fine tuning, prompting engineering, and reinforcement learning from human feedback (RLHF) are also important characteristics of LLMs.
- Fine tuning refers to further training of new data to the pre-trained model
- Prompting refers to additional input information (prompts) to the trained model
- RLHF refers to a training of a model, including human feedback in the training process
Currently, LLMs develop and improve very rapidly and many innovative applications arise in several dsicplines. Nevertheless, the development (including sustainability) of these models is extremely expensive.
Advantages
- Able to process huge amount of information and learn from it
- Applicable in a wide variety of tasks and problems (generalisaiton)
- Ability of high-performing generation alongside high accuracy
- Revolutionising the overall artificial intelligence field
Challenges
- High developmental and operational cost
- Very complex system and difficult understanding of the model’s decisions
- Requirement for immense amount of data and high computing power
- Model training process includes bias and prejudice
Usage of LLMs
Law firms may use LLM to support paraphrasing as well as translation of legal documents.
Retail companies may use the modelling tools to enhance their customer experience and support though the implementation of AI online assistance and chatbots.
Biology sciences may implement large language models to help comprehend the human body’s structural elements such as DNA, molecules, cells, proteins, and so on.
Healthcare may use LLMs to improve patient experience through AI care and wellness systems. These models may also be applied in the understanding and improvement of disease diagnostics and solutions.
Financial businesses may use the modelling tools for risk management and fraud detection.
Marketing organisations may implement LLM systems to optimise its advertising systems, improve customer comprehension, and develop more efficient marketing strategies.
Developers may use large language models to support the development of software.
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