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LIME(also: Local Interpretable Model-agnostic Explanations)

An explainable AI technique, introduced by Ribeiro et al. in 2016, that approximates any black-box model's behaviour around a single prediction by fitting a simple interpretable model (usually sparse linear regression) to perturbed versions of the input. The resulting feature…

LLM-as-Judge(also: LLM as a Judge, Model-as-Judge)

An evaluation methodology in which a large language model is prompted to assess the quality of some artifact — generated text, code, a UI, or a response from another model — according to a structured rubric. LLM-as-judge is attractive because it scales automated evaluation to…

LSTM(also: Long Short-Term Memory, LSTM Network)

A type of recurrent neural network architecture designed to learn long-term dependencies in sequential data by using special gating mechanisms that control the flow of information through the network. LSTMs are particularly effective for processing time-series data such as…

Large Vision Model(also: LVM)

A large vision model is a foundation model trained on very large image (and often video) datasets to produce general-purpose visual representations - capable of object detection, segmentation, captioning, or feature extraction without task-specific retraining. Examples include…

Large multimodal model(also: LMM, Multimodal AI, Vision-language model)

An artificial intelligence model capable of processing and generating content across multiple modalities, such as text, images, and audio. Examples include GPT-4V and Gemini. In accessibility applications, large multimodal models enable powerful new capabilities like generating…

Layer-wise Relevance Propagation(also: LRP)

Layer-wise Relevance Propagation (LRP) is an explainable AI technique that attributes a neural network's prediction back to its input features by propagating relevance scores layer by layer from the output toward the input. Unlike gradient-based saliency methods, LRP…

Learning Vector Quantization(also: LVQ)

A supervised machine learning algorithm used for pattern classification, commonly applied in brain-computer interface systems to classify EEG signals. LVQ works by creating a set of reference vectors (codebook) that represent decision boundaries between different classes of…

Linear Discriminant Analysis(also: Fisher Discriminant Analysis, Fisherfaces)

A statistical method used in pattern recognition and machine learning that finds a linear combination of features to best separate two or more classes of objects. In the context of face recognition, LDA (also known as the Fisherfaces method) projects face images into a…

LoRA(also: Low-Rank Adaptation)

A parameter-efficient fine-tuning technique, introduced by Hu et al. in 2022, in which a large pretrained neural network is specialised by training only a pair of small low-rank matrices that modify specific weight projections, while the original weights remain frozen. LoRA…

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