Category: Model Training & Tuning

Threat Vectors in Fine-Tuning

Threat vectors in fine-tuning refer to the different ways security and privacy can be compromised when adapting machine learning models with new data. When fine-tuning, attackers might insert malicious data, manipulate the process, or exploit vulnerabilities to influence the model’s behaviour. Understanding these vectors helps prevent data leaks, bias introduction, or unauthorised access during the…

Task-Specific Fine-Tuning

Task-specific fine-tuning is the process of taking a pre-trained artificial intelligence model and further training it using data specific to a particular task or application. This extra training helps the model become better at solving the chosen problem, such as translating languages, detecting spam emails, or analysing medical images. By focusing on relevant examples, the…

Semantic Entropy Regularisation

Semantic entropy regularisation is a technique used in machine learning to encourage models to make more confident and meaningful predictions. By adjusting how uncertain a model is about its outputs, it helps the model avoid being too indecisive or too certain without reason. This can improve the quality and reliability of the model’s results, especially…

Context Cascade Networks

Context Cascade Networks are computational models designed to process and distribute contextual information through multiple layers or stages. Each layer passes important details to the next, helping the system understand complex relationships and dependencies. These networks are especially useful in tasks where understanding the context of information is crucial for making accurate decisions or predictions.

Latent Representation Calibration

Latent representation calibration is the process of adjusting or fine-tuning the hidden features that a machine learning model creates while processing data. These hidden features, or latent representations, are not directly visible but are used by the model to make predictions or decisions. Calibration helps ensure that these internal features accurately reflect the real-world characteristics…

Reinforcement via User Signals

Reinforcement via user signals refers to improving a system or product by observing how users interact with it. When users click, like, share, or ignore certain items, these actions provide feedback known as user signals. Systems can use these signals to adjust and offer more relevant or useful content, making the experience better for future…

Response Temperature Strategies

Response temperature strategies refer to methods used to control how predictable or creative the output of an AI language model is. By adjusting the temperature setting, users can influence whether the AI gives more straightforward or more varied responses. A lower temperature leads to more focused and deterministic answers, while a higher temperature allows for…

ChatML Pretraining Methods

ChatML pretraining methods refer to the techniques used to train language models using the Chat Markup Language (ChatML) format. ChatML is a structured way to represent conversations, where messages are tagged with roles such as user, assistant, or system. These methods help models learn how to understand, continue, and manage multi-turn dialogues by exposing them…

ML Optimisation Agent

An ML Optimisation Agent is a computer program or system that automatically improves the performance of machine learning models. It uses feedback and data to adjust the model’s parameters, settings, or strategies, aiming to make predictions more accurate or efficient. These agents can work by trying different approaches and learning from results, so they can…