Exploring Bi-Directional Vs Auto-Regressive Models

Unleashing the power of transformers

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In the realm of artificial intelligence, the battle between bi-directional and auto-regressive transformers is rewriting the rules of language modeling. Imagine a technology that can read and understand text from all directions simultaneously versus one that predicts the next word in a sequence with unmatched precision. Bi-directional transformers, like BERT, offer a holistic view of context, while auto-regressive models, such as GPT, excel in generating coherent and contextually relevant text.

As we dive into the mechanics and implications of these two revolutionary approaches, you will discover how they each carve unique pathways through the landscape of natural language processing, shaping the future of AI in ways once thought impossible.

Table of Content

• BERT and Auto-regressive transformers
• Pre-training
• Applications
• Creating your own LLM

BERT and Auto-regressive Transformers

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Bidirectional and Auto-Regressive Transformers combines the

strength of both BERT and GPT architectures. It has a bi-directional encoder
and an autoregressive decoder, which allows the advantages of both BERT
and GPT pre-training methods. The encoder and decoder architecture of

BART is similar to the original transformer architecture. However, the pre-training objective of BART is unique compared to other models. In this

section, we will delve into the details of BART.

Pre-training

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The pre-training objective is to reconstruct the original input text after it has
been corrupted, which helps to learn the structure and semantics of the
language. Let us describe the process in detail.

1. Data collection and pre-processing — Collect, clean and pre-process
   the data. This process is similar to what you would do for pre-training
   
   with the BERT model.
2. Text corruption — Create a noise function that will be applied to the
   input text. This function should introduce various types of corruptions
   
   to the text, such as token masking, token deletion, token replacement
   and text shuffling.
3. Corrupting input text — Apply the noise function to the pre-processed
   text to generate corrupted versions of the input text.
4. Model architecture — The BART-base model consists of 6 encoders
   and 6 decoders, while the BART-large model is equipped with 12
   encoders and 12 decoders.
5. Label — The true label for BART is the input sequence without
   
   corruption.
6. Pre-training — BART’s pre-training objective is to minimize the
   difference between the reconstructed text generated by the decoder
   
   and the original, uncorrupted text. This is achieved by using a cross-entropy loss function that compares the predicted token probabilities
   at each position with the actual tokens in the original text.

Applications

While BERT excels at tasks requiring bidirectional context understanding and GPT is better suited for text generation tasks due to its auto-regressive
nature, BART bridges the gap between these two models by combining
their strengths. BART has been found to perform particularly well in the

application areas.

• Text summarization
• Machine translation
• Text generation
• Sentiment analysis
• Conversational AI
• Question-answering

if you require a large language model that excels in tasks

involving both auto-regression and bidirectional context understanding,
BART is an optimal choice.

Creating Your Own LLM

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Imagine you are building a house. You could head to Home Depot, purchase
all the pre-made kitchens, doors, and quickly assemble your home.
However, would it not be fantastic if your house design could cater to your

unique needs and desires? Additionally, you should consider cost overruns,
thus, not building all the materials from scratch. That is where creating your

own Language Model comes in.

Off-the-shelf LLMs are impressive, but they might not capture your

organization’s specific data, industry jargon, and contextual information.
This challenge is intensified if you work in a specialized industry. Let us

discuss the healthcare domain. Real clinical notes, for example, are not
available for general LLMs during pre-training due to HIPAA and
government regulations. As a result, bert-based-uncased, which was
trained with internet datasets and book datasets, does not capture the way
doctors write clinical notes. By creating your own LLM, you can optimize

your organization’s language understanding. Moreover, you will not have to
start from scratch. Creating an LLM from scratch requires hundreds of
thousands of dollars, just for GPU costs. Instead, you can take a pre-trained model and further pre-train it with your organization’s dataset. Let
us highlight some of the major benefits of creating LLMs tailored to your
organization.

1. Customized knowledge — An in-house LLM can be further trained on
   your organization’s specific data, industry jargon, and contextual
   information. This means it will understand your organization’s lingo
   
   like a seasoned employee, ensuring better performance and more
   accurate results.
2. Adaptability — You can continuously pre-train it based on the latest
   trends, emerging technologies and shifting priorities, ensuring it
   stays relevant and effective.
3. Privacy and security — You can maintain control over sensitive data
   while not sacrificing the performance of NLP.
4. Competitive advantage — The LLM tailored to your organization and
   industry can deliver insights and understanding that generic LLMs
   cannot. You will have a competitive advantage compared to industry
   peers.

Conclusion

As we wrap up our exploration of bi-directional and auto-regressive transformers, it’s evident that these models are not just technical marvels but game changers in the field of natural language processing. The power of bi-directional transformers lies in their ability to grasp context from both directions, enhancing understanding and accuracy. In contrast, auto-regressive transformers shine in their prowess for text generation, producing coherent and contextually rich outputs. Their sophisticated pre-training regimes lay the groundwork for a multitude of groundbreaking applications, from search engines to creative writing tools and as we venture into creating our own large language models, we are empowered by these insights to build upon these transformative architectures, pushing the boundaries of what AI can achieve. The future of language modeling is not just about leveraging existing technologies but also about innovating and crafting new solutions that will drive the next wave of AI advancements.