In the realm of Natural Language Processing(NLP) and conversational AI, there are several AI models that have gained attention in their ability to generate human-like text. Out of them, the two prominent models that have captured the imagination of researchers and developers are ChatGPT and Bard which have been developed by Open AI and Google respectively.
In this article, we will dive into the machine learning perspective to compare and contrast between the underlying principles, architecture, training objectives and applications of ChatGPT and Bard.
Let’s get started exploring the unique strengths and potential of these powerful models.
ChatGPT, a variant of Generative Pre-trained Transformer (GPT) series, is specially designed by Open AI in November 2022 for engaging in conversational interactions. ChatGPT excels in generating fluent, interactive and contextually relevant responses making it well-suited for applications like virtual assistants, customer support chatbots, language translation showcasing its dynamic and interactive abilities.
Exploring BARD :
BARD is a bidirectional and auto-regressive transformer model developed by Google Research making it publicly available in March 2023. It is very effective in tasks involving accurate and concise outputs such as text summarization, question answering and machine translation.
Training Process :
- Bard is trained using a technique called supervised learning. This means that it is given a large dataset of text and code and trained to generate text that is similar to the text in the dataset. This dataset includes massive collection of text and code from the internet and it also has access to the internet in real-time.
- Like Bard, ChatGPT is trained using both supervised and unsupervised learning. This means that it is given a large dataset of text and code and trained to generate text that is similar to the text in the dataset. In addition to this, it is also trained on a dataset of human feedback making it to produce an informative and comprehensive response.
The training process is still ongoing and is being improved as and when new data is available and new techniques are developed.
Understanding the Architectures :
Both ChatGPT and Bard are built on the foundation of transformer architectures revolutionising the field of NLP. Transformers incorporate attention mechanisms to capture relations between the words, enabling the model to attend to relevant information and effectively encode long-range dependencies in sequences. But however, there are differences in the specifics of the architecture leading to varied applications.
Algorithms Used :
- ChatGPT incorporates transformer architecture and uses attention mechanisms, greedy decoding, beam search and reinforcement learning.
- Bard also employs transformer architecture and uses attention mechanisms, greedy decoding, beam search and denoising autoencoding.
Let’s understand these algorithms in a simpler way :
- Attention: Attention mechanisms are used to weigh the relevance of different words in a sequence, allowing models to focus on important information and generate more contextually appropriate responses.
- Greedy Decoding: This is an algorithm used to generate text. It considers the set of all the possible next words and selects the word with highest probability at each step.
- Beam Search: Beam search is a decoding algorithm used to generate multiple potential responses and select the most likely one based on a scoring mechanism, enhancing the quality of generated text.
- Reinforcement learning: It interacts with a reward model where it improves its response based on user feedback and adjusts itself to produce the desired outcomes.
- Denoising Auto encoding: The model learns to reconstruct the original input text from a corrupted or masked version. This helps in generating coherent and accurate text.
Key differences between Bard and ChatGPT :
In the domain of natural language processing and conversational AI, ChatGPT excels in dynamic and engaging interactions, while Bard shines in text generation and summarization. Understanding the specific objectives and strengths of each model allows us to select the most suitable model for a given task or application. As machine learning advances, staying updated and considering task-specific requirements is crucial when deciding between ChatGPT and Bard for maximum utilization. If you're interested in exploring further comparisons on chatbots, be sure to check out this link.
Also checkout: If you'd like to expand your reading on chatbot testing, I recommend referring to this link for more information.
By Soumya G