Large language models (LLMs) are taking the world by storm, bringing forth unparalleled advancements in natural language processing (NLP) tasks.
However, as these models grow in size and complexity, so do the demands on computational resources and energy consumption.
Enter LoRA: Low-Rank Adaptation of Large Language Models, a groundbreaking method that enables faster, more efficient adaptation of LLMs without sacrificing performance.
In this in-depth article, we’ll explore the inner workings
The Bidirectional Encoder Representations from Transformers (BERT) is a powerful language model that uses a masked language model (MLM) pre-training objective to improve upon standard Transformers. BERT is a deep bidirectional Transformer that fuses the left and right contexts of a sentence together. Consequently, this allows for better contextual understanding of the input.
What is BERT?
BERT is a language model developed by Google that uses deep neural networks to better understand the cont
Bort: A More Efficient Variant of BERT Architecture
Bort is a superior architectural variant of BERT, an effective neural network for natural language processing. The idea behind Bort is to optimize the subset of architectural parameters for the BERT architecture via a fully polynomial-time approximation scheme (FPTAS) by fully utilizing the power of neural architecture search.
Among neural networks, BERT is one of the most effective because it is pre-trained for on a massive amount of text da
Canine: A Language Understanding Encoder
Canine is a pre-trained encoder for language understanding. It operates directly on character sequences, without explicit tokenization or vocabulary. It uses a pre-training strategy with soft inductive biases in place of hard token boundaries. Essentially, Canine is a machine learning algorithm that understands language by analyzing sequences of characters, which is different from many other algorithms that rely on pre-defined word boundaries.
Canine's
CharacterBERT is an exciting new development in natural language processing (NLP) that promises to use state-of-the-art machine learning techniques to better understand language in a variety of domains. The system is based on BERT, which stands for Bidirectional Encoder Representations from Transformers, a powerful neural network that is widely used in NLP applications. However, CharacterBERT does away with BERT's wordpiece system and instead uses a CharacterCNN module to better represent input
Since 2020, manufacturers have been steadily releasing bigger and bigger models like the GPT-3 (175B), LaMDA (137B), Jurassic-1 (178B), Megatron-Turing NLG (530B), and Gopher (280B). According to Kaplan’s law, these models are an improvement over their predecessors (GPT-2, BERT), but they still fall short of their full potential.
In their most recent paper, researchers at DeepMind dissect the conventional wisdom that more complex models equal better performance.
The company has uncovered a pre
CAM: An Overview
In recent years, computer vision has grown exponentially, with machines becoming advanced enough to identify and classify objects through deep learning and neural networks. Consequently, the interpretation of neural network decision making has become a complex task. One such technique to interpret these decisions is CAM, which stands for Class Activation Maps.
What is CAM?
CAM or Class Activation Maps is a technique that uses Convolutional Neural Networks (CNNs) to visualize
Cross-encoder Reranking: Improving Language Understanding
As technology progresses, many companies have been looking to improve their language understanding capabilities. One technique being used to do this is called cross-encoder reranking.
Cross-encoder reranking is a process that involves taking a large amount of text data and organizing it so that it can be better understood. Essentially, this involves training a machine learning algorithm to analyze two different pieces of text and determ
Cross-View Training, also known as CVT, is a modern way to improve artificial intelligence systems through the use of semi-supervised algorithms. This method improves the accuracy of distributed word representations by making use of both labelled and unlabelled data points.
What is Cross-View Training
Cross-View Training is a technique that aids in training distributed word representations. This is done through the use of a semi-supervised algorithm, which works by using both labelled and unl
CuBERT: Advancements in Code Understanding with BERT-based Models
In the world of programming, understanding code is of utmost importance. The proper understanding of programming language is the line that separates novices and experts in the field. To enable machines to understand code better, researchers and data scientists have been working to harness the power of machine learning and natural language processing (NLP) to deepen the code's understanding. Along these lines, Code Understanding B
What is DeLighT?
DeLighT is a transformer architecture that aims to improve parameter efficiency by using DExTra, a light-weight transformation within each Transformer block, and block-wise scaling across blocks. This allows for more efficient use of single-headed attention and bottleneck FFN layers, and shallower and narrower DeLighT blocks near the input, and wider and deeper DeLighT blocks near the output.
What is a Transformer Architecture?
A transformer architecture is a type of neural
What is DynaBERT?
DynaBERT is a type of natural language processing tool developed by a research team. It is a variant of BERT, a popular language model used in natural language processing tasks such as text classification, question answering, and more. DynaBERT has the unique feature of being able to adjust the size and latency of its model by selecting an adaptive width and depth.
How Does DynaBERT Work?
The training process of DynaBERT involves two stages. In the first stage, a width-adap
The Basics of Electric: A Cloze Model for Text Representation Learning
Electric is an advanced energy-based cloze model for representation learning over text, developed in the field of machine learning. It has a similar structure to the popular BERT, but with subtle differences in its architecture and functioning.
The primary purpose of Electric is to generate vector representations for text, and it uses the generative model methodology to achieve this goal. Specifically, it models $p\_{\text{
What is ELMo?
ELMo stands for Embeddings from Language Models, which is a special type of word representation that was created to better understand the complex characteristics of word use, such as syntax and semantics. It's an innovative new tool that can help researchers and developers to more accurately model language and to better predict how words will be used in different linguistic contexts.
How Does ELMo Work?
The ELMo algorithm works by using a deep bidirectional language model (biLM
ERNIE-GEN: Bridging the Gap Between Training and Inference
If you're interested in natural language processing, you may have heard of ERNIE-GEN. ERNIE-GEN is a framework used for multi-flow sequence to sequence pre-training and fine-tuning. It was designed to bridge the gap between model training and inference by introducing an infilling generation mechanism and a noise-aware generation method while training the model to generate semantically-complete spans. In this article, we'll explore ERNIE
A Feedback Transformer is a type of sequential transformer that utilizes a feedback mechanism to expose all previous representations to all future representations. This unique architecture allows for recursive computation, building stronger representations by utilizing past representations.
What is a Feedback Transformer?
A Feedback Transformer is a type of neural network architecture that is used in natural language processing tasks, image recognition, and other artificial intelligence appli
Understanding Gated Convolutional Networks
Have you ever wondered how computers are able to understand human language and generate text for chatbots or voice assistants like Siri or Alexa? One sophisticated method used to achieve this is the Gated Convolutional Network, also known as GCN. It's a type of language model that combines convolutional networks with a gating mechanism to process and predict natural language.
What are Convolutional Networks?
Convolutional networks, also known as Con
mBART is a machine learning tool that uses a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective. This means that it can learn from a variety of different languages to help with translation. The input texts are noised by masking phrases and permuting sentences, and a single Transformer model is learned to recover the texts.
What is mBART?
mBART is a machine learning tool that helps with translation by using larg
