LoRA Fine-Tuning is a method used to adjust large pre-trained artificial intelligence models, such as language models, with less computing power and memory. Instead of changing all the model’s weights, LoRA adds small, trainable layers that adapt the model for new tasks. This approach makes it faster and cheaper to customise models for specific needs…
Sparse attention models are a type of artificial intelligence model designed to focus only on the most relevant parts of the data, rather than processing everything equally. Traditional attention models look at every possible part of the input, which can be slow and require a lot of memory, especially with long texts or large datasets….
Tokenisation strategies are methods used to split text into smaller pieces called tokens, which can be words, characters, or subwords. These strategies help computers process and understand language by breaking it down into more manageable parts. The choice of strategy can affect how well a computer model understands and generates text, as different languages and…
Prompt injection is a security issue that occurs when someone manipulates the instructions given to an AI system, such as a chatbot, to make it behave in unexpected or harmful ways. This can happen if the AI is tricked into following hidden or malicious instructions within user input. As a result, the AI might reveal…
Dynamic prompt tuning is a technique used to improve the responses of artificial intelligence language models by adjusting the instructions or prompts given to them. Instead of using a fixed prompt, the system can automatically modify or optimise the prompt based on context, user feedback, or previous interactions. This helps the AI generate more accurate…
Attention mechanisms are methods used in artificial intelligence that help models focus on the most relevant parts of input data, such as words in a sentence or regions in an image. They allow the model to weigh different pieces of information differently, depending on their importance for the task. This makes it easier for the…
Transformer decoders are a component of the transformer neural network architecture, designed to generate sequences one step at a time. They work by taking in previously generated data and context information to predict the next item in a sequence, such as the next word in a sentence. Transformer decoders are often used in tasks that…
A knowledge graph is a way of organising information that connects facts and concepts together, showing how they relate to each other. It uses nodes to represent things like people, places or ideas, and links to show the relationships between them. This makes it easier for computers to understand and use complex information, helping with…
Self-supervised learning is a type of machine learning where a system teaches itself by finding patterns in unlabelled data. Instead of relying on humans to label the data, the system creates its own tasks and learns from them. This approach allows computers to make use of large amounts of raw data, which are often easier…
Catastrophic forgetting is a problem in machine learning where a model trained on new data quickly loses its ability to recall or perform well on tasks it previously learned. This happens most often when a neural network is trained on one task, then retrained on a different task without access to the original data. As…