Beyond GPT – Rise of Large Language Models

The rise of large language models in recent years has been nothing short of remarkable. These powerful tools have revolutionized the way we process and understand language, enabling us to achieve unprecedented levels of accuracy and efficiency in a wide range of NLP tasks. But what has driven this rise? It’s simple: data, hardware, and software. The availability of large amounts of annotated data has been critical in the development of these models, as it has allowed them to learn and understand the nuances of language at a deep level. Let’s continue reading to find out more about different LLMs, its use cases, and types.

What is Large Language Models?

LLMs are AI models that are trained on a large dataset of text and are capable of generating human-like text. They are designed to understand and generate natural language, and they are often used for a variety of language processing tasks, including language translation, text summarization, and question answering.

Large language models are trained using a technique called unsupervised learning, which means they are not explicitly provided with correct answers during the training process. Instead, they learn to generate text by predicting the next word in a sequence based on the words that come before it.

LLMs are often used in combination with other AI techniques, such as machine learning and deep learning, to achieve improved performance on language processing tasks.

What are type of Large Language Models (LLMs)?

Here are several examples of large language models type and their capabilities:

1. Unigram language models: These models predict the probability of a word based on its individual occurrence in the dataset.
2. Bigram language models: These models predict the probability of a word based on the previous word in the sequence.
3. Trigram language models: These models predict the probability of a word based on the previous two words in the sequence.
4. N-gram language models: These models predict the probability of a word based on the previous n-1 words in the sequence.
5. Continuous bag-of-words (CBOW) language models: These models predict a word based on the surrounding context words in the sequence.
6. Skip-gram language models: These models predict the surrounding context words based on a target word.
7. Recurrent neural network (RNN) language models: These models use a type of neural network architecture that is designed to process sequential data.
8. Transformer language models: These models use a type of neural network architecture that is designed to process long-range dependencies in sequential data.
9. Deep learning language models: These models use deep neural networks to process and generate natural language text.
10. Attention-based language models: These models use a mechanism called “attention” to weigh the importance of different words in the input sequence when generating text.

Top Alternatives Large Language Models to GPT-3

GLaM (Generalist Language Model)

GLaM was developed by Google that is designed to be more efficient and effective than other large language models like GPT-3. It is made up of different submodels or “experts” that specialize in processing different types of input data.

The model is trained on a dataset of 1.6 trillion tokens, including web pages, books, and Wikipedia articles, and is capable of performing well on a variety of natural language processing tasks, such as reading comprehension and question answering.

One of the key benefits of GLaM is that it is able to achieve competitive performance while using significantly less computation and energy than other large language models.

MT NLG (Megatron-Turing NLG):

It is a large language model developed by NVIDIA and Microsoft that has 530 billion parameters. It is made up of 105 transformer-based layers and has the ability to outperform existing models in various natural language tasks, including completion prediction, reading comprehension, and commonsense reasoning. It was trained on the Selene supercomputer using NVIDIA DGX SuperPOD.

BLOOM: 

It is an advanced artificial intelligence language model that is capable of generating text that is similar to that written by humans in 46 different languages and 13 programming languages.

It was developed by a team of over 1,000 AI researchers and is considered a top alterative to GPT-3. BLOOM was trained on 176 billion parameters, which required the use of 384 graphics cards with a memory of over 80 gigabytes each.

It was developed by HuggingFace through the BigScience Workshop and is available in different versions with fewer parameters. BLOOM can also be used to perform text tasks it was not explicitly trained for by treating them as text generation tasks

PaLM (Pathways Language Model):

PaLM is a neural network developed by Google to improve the performance of natural language processing tasks such as language translation and question answering. It has 540 billion parameters, making it one of the largest language models available.

PaLM was trained using the Pathways system, which allows it to be efficiently trained across multiple TPU (tensor processing unit) pods. It has achieved state- of-the-art performance on many language understanding and generation tasks, as well as reasoning and code-related tasks.

PaLM also demonstrates the ability to perform few-shot learning, meaning it can perform well on tasks it hasn’t been explicitly trained for by using a small number of examples.

BERT (Bidirectional Encoder Representations from Transformers):

BERT is a significantly powerful and open source natural language processing model developed by Google. It is designed to understand the context of words in a sentence by considering the words that come before and after them.

One of the main advantages of BERT is its ability to handle a large amount of data effectively. This makes it a useful tool for tasks that require a deep understanding of language, such as language translation and question answering. BERT has already been used by Google to improve the performance of its search and translation systems.

Transformer-XL:

A natural language processing model developed by researchers at Carnegie Mellon University and the Bosch Center for Artificial Intelligence.

It is designed to handle long sequences of data by using a technique called “dilated self-attention” to process the data in smaller chunks, allowing it to better capture long-term dependencies. Transformer-XL has been used for a variety of natural language processing tasks, including language translation and language modeling.

XLNet:

XLNet is a new way of training artificial intelligence systems to understand and process natural language, developed by researchers at Carnegie Mellon University and Google.

It is better than other methods because it can model the context of words in a sentence from both the beginning and the end. It can also handle a large amount of data. Other methods, like BERT, can only model the context of words in a sentence from one direction and have trouble handling long sequences of data (Read research paper).

RoBERTa (Robustly Optimized BERT Pretraining Approach):

RoBERTa is a natural language processing model developed by researchers at Facebook AI. It is build upon BERT’s technique of using masking to improve the performance of NLP tasks.

RoBERTa was created using PyTorch and it made some changes to BERT’s hyperparameters, including removing BERT’s next-sentence pretraining objective and training with larger mini-batches and learning rates.

This allowed RoBERTa to perform better on the masked language modeling objective compared to BERT and resulted in better performance on downstream tasks.

T5 (Text-To-Text Transfer Transformer):

Developed by researchers at Google, T5 is a natural language processing model that has been trained on a large dataset and is designed to perform a variety of tasks, including language translation and text summarization.

ERNIE (Enhanced Representation through knowledge Integration:

Developed by researchers at Baidu, ERNIE is a natural language processing model that has been trained on a large dataset and is designed to perform a variety of tasks, including language translation and text classification.

XLM (Cross-Lingual Language Model):

Developed by researchers at Facebook AI, XLM is a natural language processing model that has been trained on a large dataset and is designed to understand multiple languages.

Large Language Models Use Cases

Large language models (LLMs) are advanced artificial intelligence (AI) systems designed to understand and generate natural language.

They have a wide range of potential applications, including language translation, speech recognition, machine translation, sentiment analysis, chatbots, language modeling, text summarization, content recommendation, sentence completion, and text-to-speech synthesis.

LLMs are trained on vast amounts of data and use statistical models to understand the structure and patterns of language, allowing them to perform these tasks with a high degree of accuracy and naturalness.

Find out 50 mind-blowing use cases of ChatGPT

Which Algorithm is Best For Large Datasets?

There is no one “best” algorithm for large datasets, as the appropriate algorithm will depend on the specific characteristics of the dataset and the problem being solved. However, some algorithms are generally more efficient at handling large datasets than others.

For example, algorithms that are able to process data in parallel, such as many of the algorithms used in distributed machine learning systems, are generally more efficient at handling large datasets than algorithms that can only process data sequentially.

Other algorithms, such as decision tree algorithms, are able to handle large datasets without the need for parallel processing, but may be slower and less accurate than other algorithms when working with smaller datasets.

It is important to carefully consider the characteristics of your dataset and the requirements of your problem when selecting an algorithm.

In summary, there are several large language models with billions of parameters that have been developed by various organizations, including CALM, AlexaTM, LaMDA, ChinChilla, ESMFold, Gato, and WuDao2.

These models are known for their ability to perform well on various natural language processing tasks and require a strong understanding of machine learning and programming in languages like Python to be used effectively.