Not only GPT. What are the best NLP GPT alternatives worth looking into?

The NLP market is projected to reach $49.4 billion by 2027, growing at a CAGR of 27.5%. [1] This comes as no surprise, considering how far natural Language Processing (NLP) has come in recent years. [2] The past decade has seen an accelerated development of powerful NLP models that can understand and generate human-like text with unprecedented accuracy. One such model that’s making waves in the technology and creative sector is OpenAI’s ChatGPT.

This model can generate human-like text to resolve complex coding problems. And as the demand for AI-based creativity and personalization tools increases, several other players like Google and DeepMind have also developed their own NLP models with the potential to rival and even outmatch ChatGPT’s capabilities.

This article will explore some of the best NLP models giving ChatGPT a run for its money. Read on for more insights NLP solutions.

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The best NLP model GPT alternatives

There are many NLP models out there, but we’ve made a list of what we consider the best NLP model alternatives for ChatGPT. Let’s have a look at them:

BERT

BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based neural network model developed by Google in 2018. The model was trained on a large dataset through a masked language modeling process that enables it to predict words in a sentence and learn general-purpose language representations.

Source: towardsdatscience.com

These unique qualities mean that the model can be fine-tuned for various NLP processes like language translation, question answering, and text classification. But how, exactly, does it do it? Well, BERT was created based on a transformer architecture, a type of artificial neural network architecture that uses self-attention mechanisms to process and generates input and output sequences, respectively [3].

One of the key features of BERT that really sets it apart from other natural language processing models is its ability to consider the context of the words on both the right and left sides of each word in any given sequence. This ability enables it to understand the meaning of individual words in a sentence in relation to the words around them, thus making it perfectly suited to tasks that require contextual understanding, like question answering. BERT is currently available as an open-source library, which has propelled its widespread adoption across the NLP community.

Unfortunately, its enormous size and training methods used during its development present a few limitations, like huge computation requirements, which make it quite expensive to train and operate.

GPT-3

A list of NLP language models similar to ChatGPT wouldn’t be complete without its distant cousin from the same company, OpenAI. GPT-3 (Generative Pre-Trained Transformer 3) is a neural network-based language generation model. With 175 billion parameters, it’s also one of the largest and most powerful NLP language models created to date.

Source: medium.com

Like ChatGPT, GPT-3 was trained on a huge dataset of text. That, along with its outstanding number of parameters, gives GPT-3 the ability to perform a wide range of NLP tasks such as language translation, question answering, text summarization, and text completion.

One of the model’s most notable capabilities is generating human-like text that is almost indistinguishable from human-generated content. This makes it suitable for numerous business applications, including generating marketing materials, creating chatbots, and generating code.

Read more about GPT-3 vs. ChatGPT. The Key Differences

Hugging Face

Hugging Face takes a rather different approach to NLP compared to other models on this list. Rather than being an actual NLP model, Hugging Face is a platform that provides tools for training and deploying natural language processing models.
This NLP model consists of a large collection of pre-trained NLP models that can be fine-tuned for various tasks such as question answering, language translation, and text classification.

Hugging Face is designed for user-friendliness and ease of use, focusing on flexibility and usability. Its wide assortment of tools and libraries makes it easy to train and deploy NLP models. When leveraged correctly, you can also use it for data processing and visualization.

One of the major perks of using Hugging Face is it has a large, active community of users and developers and is constantly updated with new improvements and features. This makes it especially suitable for developers and researchers looking for a powerful and easy-to-use library for training and deploying NLP models.

XLNet

XLNet is a product of collaboration by a group of researchers from Google and Carnegie Mellon University. It is a generalized pre-trained autoregressive model that leverages transformer architecture and autoencoding capabilities to give it all the perks of transformer-based models without their limitations.

To put this into context, XLNet leverages the bidirectional context analysis of transformer-based models like BERT, then takes it a step further by calculating the likelihood of a sequence of words based on all possible permutations.

RoBERTa

RoBERTa (Robustly Optimized BERT Pre-training) is a product of research conducted by researchers from the University of Washington and Facebook AI. The model is basically an optimized version of BERT, built specifically to enhance its performance and overcome some of its weaknesses.

Source: reserachgate.net

BERT’s bidirectional context analysis training model works pretty well in analyzing the context of individual words within a sentence. Unfortunately, this training method also presents a few limitations.

In a bid to eliminate these limitations and enhance its capabilities, the research group analyzed the training model’s performance and discovered that they could enhance its performance by using a larger dataset of training data and removing the next sentence prediction from the training process. RoBERTa is a product of these modifications.

ELSA

ELSA (Explicit Language Structure Aware) is an AI-based natural language processing model developed by Google in 2020. Developers at Google used explicit language structure representations to guide the model during the pre-training stages of its development. This gives the model a better understanding of the context of words within a sentence, giving it better performance on tasks that require the understanding of complex language structures.

The model also uses a self-supervised task called coreference resolution prediction (CRP). Like with explicit language structure representations, CRP helps the model identify when two or more expressions in a text refer to the same entity, thus giving it a better understanding of context.

In terms of performance, ELSA has shown pretty impressive results on a number of NLP understanding benchmarks, including the CoNLL-2012 shared task on coreference resolution and the SuperGLUE benchmark for natural language understanding.

Replika

Unlike other NLP language models on our list, Replika takes a different approach to NLP by simulating human conversation and providing support. This makes it an essentially AI-powered chatbot. The model can learn and adapt to a user’s personality, preferences, and language patterns, thus enabling it to make the conversation feel more natural and personalized.

Source: blog.replika.com

Users can also directly customize Replika’s personality and other preferences, such as making it more or less talkative or more open to discussions on sensitive topics. This, along with the models’ real-time speech recognition and feedback, makes it a pretty effective tool for emotional support. With that said, Replika is not a viable replacement for human interaction or professional help.

DialoGPT

DialoGPT (Dialogue Generative Pre-Training Transformer) is a close cousin to ChatGPT. The pre-trained NLP language model is a product of collaboration between Google and OpenAI. DialoGPT is based on transformer architecture and was trained on a massive dataset, which included text data and a diverse range of conversational data from various sources such as web pages, books, and dialogs.

Among its most notable features is its ability to generate coherent and fluent responses to different prompts. This makes it especially suitable for NLP tasks such as language translation, text summarization, and chatbot applications.

You can access DialoGPT through the OpenAI API, which allows developers to easily integrate the language model into their applications.

So, if ChatGPT is not for you, there are eight other best NLP models you can pick for your project. Now, let’s take a look at the future of NLP language models. It’s promising!

The future of NLP language models

Natural language processing has come a long way since the 1950s; when Warren McCulloch and Walter Pitts developed the McCulloch-Pitts model [4]. Over the course of AI evolution, several other bigger, better, and more efficient models like GPT have been developed.

As the demand for more personalized and specialized models increases, the future of NLP models is likely to involve a continued focus on improving performance and language processing capabilities, which may fuel advancements in personalization, language understanding, explainability, robustness, and safety, as well as multi-modal capabilities, thus enabling future models to understand and generate text, audio, and video responses.

Final thoughts on the best NLP models

GPT has revolutionized how humans communicate with machines and paved the way for further advancements in NLP technology. However, despite its popularity, it’s not the only NLP language model out there. Take the examples discussed above, for instance. These are some of the best NLP models that have similar, if not better, capabilities than GPT. Check out our Generative AI development services.

References

[1] Marketsandmarkets.com, Market Reports, NLP. URL: https://bit.ly/3j5icLc. Accessed January 21, 2023
[2] Springer.com. URL: https://link.springer.com/article/10.1007/s11042-022-13428-4. Accessed January 21, 2023
[3] Mdpi.com. URL: https://www.mdpi.com/2078-2489/13/2/69. Accessed January 21, 2023
[4] Home.csulb.edu. URL: https://home.csulb.edu/~cwallis/artificialn/History.htm. Accessed January 21, 2023