Top 10 Leading Language Models for NLP in 2023
The top leading language models for NLP in 2023 are trained to understand data in text or audio format.
NLP or Natural Language Processing is a subfield of AI that is a significant tool for reading, deciphering and understanding human language. NLP allows machines to imitate human intelligence impressively. The NLP language models in 2023 are trained to understand data in text format, like PDF or audio format, like voice commands.
Language models are the key for building NLP applications. AI and Machine learning developers and researchers trust pre-trained language models to build NLP language models. The language models utilize the transfer training technique wherein a model trained on one dataset to perform a task, then the same model is repurposed to perform different NLP functions on a new dataset. Large language models or LLM like GPT-3 and BERT perform complex tasks by crafting input text in a way that triggers the model. The use of NLP technology is widespread among numerous industries and here are the top 10 Leading language models for NLP in 2023:
BERT or Bidirectional Encoder Representations from Transformers is a technique developed by Google for NLP pre-training. It utilizes a neural network architecture, Transformer for language understanding. The language model is suited for tasks like speech recognition, text-to-speech transformation etc, any task that transforms input sequence into output sequence. 11 NLP tasks can be efficiently performed using BERT algorithm. Google Search is the best example of BERT’s efficiency. Other applications from Google, such as Google Docs, Gmail Smart Compose utilizes BERT for text prediction.
ChatGPT-3 is a transformer-based NLP model that perform tasks like translations, answering questions and many more. GPT-3 with its recent advancements hell to write news articles and generate codes. Unlike other language models, GPT-3 does not require fine-tuning to perform downstream tasks. It can manage statistical dependencies between different words. The reason for considering GPT-3 to be one of the biggest pre-trained NLP model is because it is trained on over 175 billion parameters on 45 TB of text sourced from all over the internet.
OpenAI’s GPT-2 demonstrates that language models begins to learn tasks like answering questions, translation, reading, summarisation etc, without explicit supervision. It is trained on a new dataset of millions of web pages called WebText. The model handles a wide variety of tasks and produce promising results. It generates coherent paragraphs of text and acheives promising, competitive results on a wide variety of tasks.
RoBERTa or Robustly optimized BERT pre-training approach is an optimized method for pre-training a self-supervised NLP system. The system builds it’s language model on BERT’s language masking strategy by learning and predicting intentionally hidden sections of text. RoBERTa is a pre-trained model which Excel all tasks on GLUE or General Language Understanding Evaluation.
ALBERT is a lite version of BERT which is presented by Google to deal with issues emerging because of increased model size which leads to slower training times. This language model was designed with two parameter-reduction technique: Factorized Embedding and Cross-Layer Parameter Sharing. In Factorized embedding, hidden layers and vocabulary embedding are measured seperately. Whereas, the Cross-Layer Parameter Sharing prevents the number of parameters from increasing as the network grows.
There are language models that use denoising autoencoding like BERT that perform better than models that use autoregression methods. XLNet uses autoregression pre-training which allows students to learn bidirectional context and overcome the limitations of BERT.
T5 or Text-to-Text Transfer Transformer emerged as a powerful NLP technique to train model on data-rich task before being fine-tuned on a downstream task. Google has suggested a unified approach to transfer learning in NLP to set a new state of art in the field. This model is trained using web scraped data to come up with state-of-the-art results on several NLP tasks.
ELECTRA or Efficiently Learning an Encoder that Classifies Token Replacements Accurately is a Masked language modelling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with MASK and then train a model to reconstruct the orginal tokens. They generally require a large amount of computer power, thus, they tend to produce good results when applied to downstream NLP tasks.
DeBERTa or Decoding-enhanced BERT with disentangled attention was proposed by Microsoft Researchers with two main improvements over BERT namely disentangled attention and an enhanced mask decoder. DeBERTa features an enhanced mask decoder, which gives the decoder both the absolute and relative position of the token or word.
StructBERT is a pre-trained language model with two auxiliary tasks to make the most of the sequential order of words and sentences, which leverage language structures at the word and sentence levels, respectively. As a result, the new model is adapted to different levels of language understanding required by downstream tasks.