Which AI Model Is Best For You- Comparing ChatGPT And Google BERT

In recent times, Natural Language Processing (NLP) has gained significant prominence, with notable models like ChatGPT and BERT standing out. BERT, or Bidirectional Encoder Representations from Transformers, is an open-source neural network developed by Google for NLP.?

In contrast, ChatGPT, a sibling model of Instruct GPT, is an advanced language model by OpenAI, based on GPT 3.5 and GPT 4.0 foundations. Capable of responding to queries and aiding in various tasks, this article explores the similarities and differences between these influential models.

What is BERT?

BERT, short for Bidirectional Encoder Representations from Transformers, is a pre-trained Natural Language Processing (NLP) model built on the transformer architecture. This architecture comprises transformer blocks with sub-modules, including the self-attention mechanism and position-wise feed-forward neural network.

Google's BERT, employing the transformer architecture, excels at comprehending input text contextually by processing it bidirectionally. It analyzes words within the input text by considering both preceding and succeeding words. Leveraging state-of-the-art Cloud TPUs, Google has developed advanced models that yield more relevant results swiftly.

BERT finds application in obtaining ranked and featured snippets in Search, showcasing superior performance in various NLP tasks. Pre-trained on extensive text data, it learns language patterns and structures, resulting in a language model with a more human-like understanding due to pre-training.

This model proves particularly beneficial for intricate searches and queries involving prepositions like 'for' and 'to,' as it excels at grasping the context of words in such queries.

What is ChatGPT?

ChatGPT is an Natural Language Processing (NLP) model utilizing transformer architecture and deep learning algorithms to generate human-like responses to user queries. Trained in Reinforcement Learning from Human feedback, it excels in understanding query context and providing contextually relevant replies.

The initial model underwent supervised fine-tuning, where AI trainers engaged in conversations from both user and AI perspectives, utilizing model-written suggestions to create responses and generate a dataset. This dataset, combined with a previous model's database, formed the dialogue format for ChatGPT.

Like BERT, ChatGPT is pre-trained on extensive data, allowing it to produce remarkably accurate results with minimal prompts. It doesn't rely on fixed patterns or rules, making it a powerful choice for crafting chatbots and virtual assistants.

In 2023, OpenAI introduced the latest GPT model update, GPT-4. GPT-4 showcases enhanced creativity and can generate, edit, and iterate on technical writing tasks. Addressing a limitation of ChatGPT, GPT-4 can handle up to 25,000 words, proving highly valuable for long-form content. This updated model delivers more reasonable output compared to ChatGPT.

Some Drawbacks of Large NLP Models

Despite their impressive capabilities, large NLP models come with inherent disadvantages, including:

Lack of Authenticity: While responses may appear genuine, verifying the authenticity of output from generative models proves challenging.

Prompt Sensitivity: These models exhibit sensitivity to prompts, providing different responses for similar prompts with minor modifications. For instance, a slight tweak in wording can alter the model's response to the same question.

Biased Answers: Language models may reflect biases present in the training data, leading to answers that appear somewhat repetitive and influenced by the data they were trained on.

Distinguishing BERT from ChatGPT

Understanding both NLP models allows us to highlight key differences based on their functionalities and user profiles.

Level of Customization:

• ChatGPT: Highly customizable, as it is trained on specific models, enabling multiple use cases.
• BERT: Less customizable than ChatGPT, as it is trained on a more general-purpose model.

Text Processing:

• ChatGPT: Utilizes a general transformer architecture to process text and provide contextual-based responses.
• BERT: Employs bidirectional transformers to grasp the context of input text.

Architecture and Model Size:

• BERT: Comes in different variations; the basic model features 12 transformer layers, 110 million parameters, and 768 self-attention training heads. BERT large, another variation, includes 24 transformer layers, 340 parameters, and 1024 self-attention training heads.
• ChatGPT: A large model with 96 transformer layers and 175 billion parameters. The latest update, GPT-4, boasts almost 100 trillion parameters.

Access to the Models:

• BERT: Open source, enabling anyone to train it for custom applications. Google indicates approximately 30 minutes for training using a single Cloud TPU or several hours with a single GPU.
• ChatGPT: OpenAI provides various GPT model variations, some free to use and others available through paid plans.

Objective of Training:

• BERT: Initially pre-trained on extensive data, then fine-tuned for specific downstream tasks, focusing on Natural Language Understanding (NLU) and Natural Language Generation (NLG).
• ChatGPT: Created to facilitate more human-like interactions and responses in conversations. Fine-tuned using reinforced learning and trained with human feedback.

Real-World Application:

Both models serve distinct purposes.

• BERT: Primarily focuses on language understanding, leveraging bidirectional transformers to comprehend both left and right context.
• ChatGPT: Emphasizes generating and comprehending conversational text. Its generative transformers allow it to retain the context of the conversation, producing output based on that context.

Wrapping Up

In the expansive realm of deep learning and Natural Language Processing (NLP), Google's BERT and OpenAI's ChatGPT stand out as powerful models tailored to specific use cases. Utilizing transformer architecture and pre-training techniques, both models grasp general language patterns to generate human-like outputs. By comprehending the commonalities and distinctions between these models, you can make an informed decision, selecting the model that aligns with your specific use case.