If you’re a Natural Language Processing (NLP) enthusiast, you’ll notice two concepts making rounds — BERT and LLM.
BERT stands for Bidirectional Encoder Representations from Transformers, whereas LLM stands for Large Language Model. These models have improved NLP in their own way — through their strengths and weaknesses.
In this article, we’ll look closer at both BERT and LLM and what they have to offer. Let’s get started.
BERT – More Accurate and Powerful
Google developed BERT to allow better context understanding and learning through words. It does it by doing a bidirectional approach where it learns about a word context by learning about the surrounding words (left and right). It overcomes the limitations of older models capable of only reading the word on the right.
BERT uses a transformer-based model, which is at the core of the rapid growth of the NLP research area. Due to a clever mix of context-based understanding of semantics, BERT offers high accuracy and excels at answering specific questions or entities. So, if a business or organization wants a highly accurate and context-heavy model to answer queries, then BERT is the way to go.
Internals of BERT
Technically, BERT uses a bidirectional transformer with two core objectives: Next-sentence prediction and Masked Language Model (MLM). As BERT is bidirectional, the model semantic learning takes place from left to right and right to left simultaneously.
Due to heavy dependency on learning, BERT requires pre-training with tons of specific task data. Without proper pre-training, BERT might not perform at the expected level of accuracy.
LLM — Fundamental To NLP Tasks
Large Language Models use a statistical model that predicts the sequence of words. This gives LLM a wider ability to accomplish fundamental NLP tasks. For example, AI text generators use LLM models to generate human-like text. It is also effective in speech recognition and machine translation.
Unlike BERT, the language model can handle queries with a high dependency on text. With the higher capacity to remember the context, the user can interact with LLM models in more detail giving it the ability to solve complex problems that need to remember the context for a longer period of time.
Internals of LLM
Inside LLM, you’ll find the use of Long Short-Term Memory (LSTM), a recurrent neural network with memory cells capable of storing and retrieving information with long-memory capabilities. LLM easily overcomes short-term memory limitations.
If you look closely, you’ll notice that most LLMs are capable of generating text and, hence, require a lot of pre-training text to become more accurate. LLM also uses deep learning as a way to understand patterns from the given data. Once the LLM is trained, it is now capable of aiding the user in its day-to-day tasks. These patterns and connection recognition help identify patterns to generate new content.
BERT Applications and Limitations
BERT has tons of applications in the field of NLP. Some of the notable ones include the following:
- Compare sentences to measure semantic similarity.
- Classify text based on classification.
- Use BERT to understand user’s query context to give them better results.
- Carry out aspect-based sentiment analysis.
- Provide accurate recommendations to users based on input description.
However, it does have limitations that you must know about. These limitations include the following:
- You need to invest a lot of training time and computational resources to make BERT work.
- BERT struggle with auto-regressive tasks, i.e., predicting tokens during interference.
- BERT has only 512 tokens maximum input length limiting its use cases.
LLM Applications and Limitations
LLM offers a wide variety of applications, which includes the following:
- Improved search engine results with better context understanding.
- Improved AI bots and assistant performance, offering retailers the ability to do customer service.
- LLMs’ ability to get pre-trained with a large set of diversified data makes it excellent at translation.
- Google’s SecPALM LLM can learn about script behavior and identify malicious behavior.
- Excellent choice for a unique content creation
- Offers code generation, code completion, and bug detection.
Comparing BERT and LLM — should you choose BERT or LLM?
Choosing between BERT and LLM depends on your requirements. Both NLP models excel at what they do. So, it is up to you to choose the one that fits your needs.
For example, if you want a model that excels in semantics (bi-directional context) and language context understanding, then BERT serves your purpose. It can perform well in different NLP tasks where you need to do sentiment analysis, entity recognition, or question-answering. However, before you choose BERT, you must be vary that it requires a lot of pre-training-specific data. It must be domain-specific as well. Another thing that you must be vary of is the computational resources. BERT requires significant computational resources.
LLM, on the other hand, is a good pick if you’re looking for a less computational-hungry language model. LLM also fits well for use cases where you have a limited data set, unspecific to any particular domain. This makes it a great pick for NLP tasks such as speech recognition. As LLM can remember information longer, it is also a great pick for any task that requires context remembering.
Conclusion
In the world of NLP, both BERT and LLM offer unique capabilities. Both of them have their limitations, but most importantly, they have unique abilities to solve crucial NLP problems. BERT is an excellent NLP model capable of offering bidirectional learning. Due to a deep understanding of semantics and context, it gives users the tool to support powerful task handling.
LLM, on the other hand, offers a more relaxed approach with access to long-term context-remembering without the need to be computationally heavy.
