BERT is indeed a type of large language model (LLM) . LLMs are essentially computer programs trained on massive amounts of text data to understand and process human language. BERT stands for “Bidirectional Encoder Representations from Transformers,” which refers to its specific technical architecture [2].
Here’s a breakdown of the relationship between BERT and LLMs:
- LLMs: These are powerful models capable of various tasks like sentiment analysis, text generation, and summarization. They are trained on huge amounts of text data .
- BERT: This is a specific LLM developed by Google in 2018 that excels at understanding the context of words in a sentence. BERT is one of the first successful models of its kind and laid the groundwork for many other LLMs.
So, BERT is a prominent example of a large language model
What makes BERT different from current LLMs?
BERT, while a foundational LLM, has some key differences compared to current models:
BERT: Primarily excels at understanding “context” and generating contextual representations for text . It was a breakthrough in this area.
Current LLMs: Many current LLMs, like GPT-3 , have a broader focus. We can not only handle context well but also perform tasks like writing different kinds of creative content and answering your questions in an informative way .
Training Approach:
BERT: Uses a bidirectional approach. This means it considers both the preceding and following words in a sentence to understand context, unlike earlier models . However, it doesn’t use reinforcement learning techniques common in some current LLMs .
Current LLMs: Many current models incorporate unsupervised learning** techniques like masked language modeling, similar to BERT. However, they may also leverage reinforcement learning to refine their outputs based on human feedback . This can help them become more aligned with human goals and less prone to factual errors.
Applications:
BERT: Due to its strength in understanding context, BERT is often fine-tuned for specific NLP tasks like question answering or sentiment analysis .
Current LLMs: Current LLMs have a wider range of applications due to their broader skillset. They can be used for tasks like generating different creative text formats, chatbots, and summarization, in addition to tasks BERT excels at .
Overall, BERT played a pivotal role in the development of LLMs, particularly with its focus on context. However, current LLMs often build upon these concepts and incorporate additional techniques to achieve more comprehensive capabilities.
