Recently, the number of technical terms related to generative AI has increased. I’ve organized each term and will introduce them in this blog.
LLM (Large Language Model)
Description: Large Language Models are trained on vast amounts of text data and perform natural language processing (NLP) tasks. An example is the GPT (Generative Pre-trained Transformer) series.
Uses: Text generation, summarization, question answering, translation, etc.
VLM (Vision-Language Model)
Description: Models that handle both visual and textual information, processing text related to images and videos. For example, they generate image captions or perform visual question answering (VQA).
Uses: Image captioning, image search, visual question answering, etc.
LVM (Latent Variable Model)
Description: Latent Variable Models assume latent variables behind observed data and use them to model the data. Typical examples include Gaussian Mixture Models (GMM) and Variational Autoencoders (VAE).
Uses: Data clustering, generative models, anomaly detection, etc.
LMM (Linear Mixed Model)
Description: Linear Mixed Models include both fixed effects and random effects, applied to hierarchical structures and correlated data.
Uses: Data analysis in biostatistics, economics, psychology, etc.
MLLM (Multilingual Language Model)
Description: Multilingual Language Models are trained in multiple languages and perform tasks such as translation and NLP across different languages.
Uses: Multilingual translation, multilingual question answering, multilingual text generation, etc.
Generative AI
Description: Generative AI refers to AI technologies that generate new data, including images, text, speech, and video. This includes techniques like GANs (Generative Adversarial Networks) and VAEs.
Uses: Image generation, text generation, speech synthesis, data augmentation, etc.
Foundation Model
Description: Foundation Models are large-scale, pre-trained models that can be adapted to a wide range of tasks. They serve as a base for various downstream tasks.
Uses: Diverse NLP tasks, visual recognition, generative tasks, etc.
These terms may overlap in usage, but each refers to specific technologies or applications, so understanding them in context is important.
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