Large language models (LLMs) are artificial neural networks that interpret natural language. Trained on large datasets, they can classify and generate texts and address queries. These models can improve and achieve accuracy and excellent performance over time.
LLMs capture the nuances of language to understand the context. Robust interpretation leads to accurate translations, better classification, and natural text generation. Moreover, it performs NLP tasks like machine translation and text summarization.
Here are a few LLMs developers must consider:
1. Generative Pre-Trained Transformer 4, 3, 2 (GPT-4, GPT-3, GPT-2)
GPT-4 is a multimodal LLM refined with reinforcement learning from human and AI feedback. The model can ingest images and text as input.
While GPT-4 enhances ChatGPT, it retains a few issues. Moreover, OpenAI has declined to reveal other technical details about the models.
GPT-3 is an NLP model with 175 billion parameters. It can generate human-like responses to prompts, paragraphs, and complete sentences. With pre-training, it performs NLP tasks like machine translation and text summarization.
GPT-3’s earlier version GPT-2 has minimal parameters but still achieves impressive results on standard NLP tasks.
Bidirectional Encoder Representations from Transformers (BERT) is a pre-trained NLP model. Businesses can use them for sentiment analysis, question answering, and text classification.
It generates contextualized word embeddings. It allows them to generate embeddings for words per context within a sentence.
It has a bidirectional transformer architecture. It allows the model to generate embeddings for a word’s left and right contexts.
Embeddings from Language Models (ELMO) is quite like BERT. It uses a bidirectional language model to capture the dependencies between words.
The model uses these dependencies to generate embeddings for each sentence’s context. ELMO analyzes sentiments, classifies text, and answers queries.
Robustly Optimized BERT Approach (RoBERTa) is BERT’s variation trained on an advanced text corpus with training approaches. Besides achieving NLP benchmarks, its training includes pre-processing steps. It enhances the model’s ability to understand and process natural language.
5. Text-to-Text Transfer Transformer (T5)
Google’s pre-trained NLP model, Text-to-Text Transfer Transformer (T5), is refined for vital tasks. It uses a transformer-based architecture. It allows it to handle long text sequences.
A Lite BERT (ALBERT) is a quick and lite version of BERT that maintains its performance on various NLP tasks. It uses the latest training approaches. This lowers the parameter numbers while maintaining the performance as BERT.
eXtreme Language Understanding Network (XLNet) is a pre-trained NLP model. It uses an autoregressive method to create contextualized representations. It has achieved robust NLP benchmarks.
Universal Language Model Fine-tuning (ULMFiT) is a pre-trained NLP model refined for various downstream tasks. The model uses a transfer learning approach. It adopts the underlying structure of natural language.
Distilled BERT (DistilBERT) is a quick and mini variant of BERT trained with advanced techniques. It minimizes the model’s size and computational requirements.
Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA) is a pre-trained NLP trained using a novel method. It replaces a subset of input tokens with synthetic tokens created by another neural network. This enhances its ability to capture representations of natural language.
Framework for State-of-the-Art Natural Language Processing (Flair), a pre-trained NLP model. It combines several neural network architectures to perform NLP tasks. It allows users to train their custom NLP models using its pre-trained architectures and embeddings.
Giant Switching Gated Hierarchical Attention for Multi-task and Large-scale Learning (GShard) uses a hierarchical attention approach to generate contextualized representations of natural language. Its designs let it handle massive data and perform many NLP tasks simultaneously.
Conditional Transformer Language Model (CTRL) can generate text conditioned on a specific context or topic. It achieves this by allowing the user to input a set of prompts.
Decoding-enhanced BERT with Disentangled Attention (DeBERTa) uses disentangled attention approach. It enhances its ability to generate better representations of natural language.
MobileBERT, BERT’s mini version, is optimized for mobile devices by minimizing many parameters. It uses the latest techniques that enhance its efficiency and maintain its performance on NLP tasks.
XLM-RoBERTa is a cross-lingual language model. Pre-trained by Facebook- AI Research, on a diverse text, it can understand and generate text in many languages.
It uses advanced training approaches that enhance its ability to understand and generate natural language across languages.
17. Universal Language Model (UniLM)
UniLM is a pre-trained NLP refined for various downstream tasks. It combines uni-directional and bi-directional transformers to capture left and right word contexts.
Anthropic’s Claude is an advanced AI assistant. It performs many NLP tasks like summarization, coding, and writing. Claude comes in two modes. First is the complete, high-performance model. The second one is a faster and lower-quality version, Claude Instant.
19. Pathways Language Model (PaLM)
Google built Pathways Language Model (PaLM) on the Pathways AI architecture. The framework’s design creates models that adopt several tasks and learn new ones.
It offers 540 billion parameters and is available via API. Interestingly, it generates explanations for complex scenarios and many logical steps.
20. FLAN UL2
Flan-UL2 is a T5 model’s upgraded version. The model is trained with Flan and is an encoder-decoder model. It has an Apache-2.0 license. Businesses can self-host or fine-tune the model.
If businesses find 20 billion parameters of Flan-UL2 excessive, they can consider the previous versions of Flan-T5. It comes in five sizes ideal for specific needs.
27. Stanford Alpaca
Stanford’s Alpaca model is based on Meta’s LLaMA 7B model. The model offers over 52,000 instruction-following demonstrations. It provides an open-source alternative to OpenAI’s GPT-3.5 models.
Alpaca’s license restricts commercial use, making it ideal for research or personal projects. With methods like LoRA, businesses can fine-tune the model on consumer-grade GPUs. It can even run on a Raspberry Pi.
Megatron-Turing Natural Language Generation (MT-NLG) is Nvidia and Microsoft’s language with 530 billion parameters. Users can access the model only through API, restricted to specific applications.
MT-NLG uses the transformer-based architecture of Megatron. It generates contextually relevant and coherent text for various tasks. The tasks include reading comprehension, natural language inferences, and word sense disambiguation.
DeepMind’s Gato, a multimodal LLM, performs tasks like image captioning and controlling a robotic arm.
Gato is a generalist model like GPT-4 capable of working on text and other modalities such as images. But, DeepMind has not released the model itself; an open-source project focuses on replicating Gato’s capabilities.
The development of LLM models has changed how computers process and interprets human language.
These models help to build chatbots, language translators, and sentiment analyzers with accuracy.
The increasing demand for efficient LLM, NLP continues to play a vital role in shaping AI’s future and the way users interact with machines.