Generative AI for NLP(RoBERTA vs. BERT vs. XLNet
Although I don’t spend a lot of time diving into code, I find a unique pleasure in occasional coding sessions. Beyond the enjoyment, coding grants me a deeper understanding of the underlying mechanisms of models. A few years back, I shared a video on YouTube where I tested sample data to compare the efficiency of pre-trained RoBERTa, BERT, and XLNet as Generative AI models. For those interested, you can watch it here.
Generative AI, especially in the realm of natural language processing, has witnessed monumental strides over the past few years. There’s an abundance of sources for pre-trained models. As of tonight (September 23rd, 2023), Hugging Face alone boasts a staggering 339,564 models and 63,490 datasets. The allure of pre-trained models lies in their ready-to-use nature, sidestepping the expenses tied to intensive computational power. However, for medium to large enterprises, these off-the-shelf solutions often fall short. The reason? A pressing need for customization to cater to specific client or user requirements.
During my tenure strategizing for Deloitte’s Generative AI (GenAI) team, I had a chance to collaborate with OpenAI to grasp their API offerings and ensure alignment with Deloitte’s objectives. A key revelation from this partnership was the inability to simply “plug and play.” Tailoring and customization were paramount. In many instances, we initiated projects from the ground up, in-house. While existing models provided inspiration, they seldom met our needs wholly.
Interestingly, this need for bespoke solutions presents a silver lining. Clients benefit from unparalleled, tailor-made solutions that offer a competitive edge, while vendors find avenues to craft specialized products, reinforcing their unique market position and bolstering business expansion.
Regardless of AI’s advancements, customization remains indispensable for establishing a distinctive market presence. And in today’s competitive landscape, uniqueness serves as a potent tool.
Now, diving deeper into the models I mentioned earlier:
BERT (Bidirectional Encoder Representations from Transformers):
- Originated from Google’s labs.
- Utilizes a transformer architecture to interpret text bidirectionally.
- Pre-training encompasses masked language modeling and next sentence prediction.
RoBERTa (A Robustly Optimized BERT Pretraining Approach):
- A brainchild of Facebook AI and envisaged as an enhanced version of BERT.
- Streamlined by removing the next sentence prediction task, and by leveraging larger datasets, longer training durations, and bigger batch sizes.
- Incorporates dynamic masking for refined contextual relationships.
- Consistently outshines BERT in various evaluations.
XLNet (Generalized Autoregressive Pretraining for Language Understanding):
- Jointly developed by Google Brain and Carnegie Mellon University.
- Merges the strengths of BERT and Transformer-XL.
- Utilizes permutation-based training to predict each token, given all possible contextual permutations.
- Bypasses BERT’s limitations in masked word predictions.
- Upon release, it surpassed BERT’s performance metrics across several NLP tasks.
In summary, these models epitomize the rapid advancements in Transformer architectures for NLP. Each brings its own set of innovations, pushing the boundaries of what’s possible in the domain of language understanding.
The Author: Dr. Ray Islam serves as a Cyber Security Lecturer at the University of Maryland, College Park, and teaches Generative AI (NLP) as an Adjunct Professor at George Mason University, VA. With a distinguished career, he has held leadership roles in AI and ML at notable firms like Deloitte, Raytheon, and Lockheed Martin. He has consulted for NASA, GSA, Berkshire Hathaway, and American Institutes for Research (AIR) among others. In his last leadership role at Deloitte, he spearheaded strategies for the Generative AI and Model Foundry team. Dr. Ray boasts a PhD from the University of Maryland, College Park, and holds degrees from Canada, Scotland, England. He’s the Editor-in-Chief for the upcoming International Research Journal of Ethics for AI, an associate editor of Journal of Prognostics and Health Management (IJPHM), published by Carleton University, Canada, and a reviewer of the journal of Reliability Engineering and System Safety, published by Elsevier. His primary research areas are Generative AI, XAI, and AI ethics.
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