What Is BERT Language Model? Its Advantages and Applications

The emergence of ChatGPT has introduced advanced LLM models, such as BERT, GPT-4, and Gemini, to support businesses in various natural language tasks. As one of the first LLM models, BERT has revolutionized AI applications with thousands of open-source, accessible, and pre-trained BERT models.

Read on to explore key components, use cases, and applications of BERT models.

What Is BERT?

BERT language model, standing for Bidirectional Encoder Representations from Transformers, is an open-source learning framework. Specifically, it performs natural language processing tasks using transformer layers. The BERT framework undergoes a pre-trained process with Wikipedia text and can be fine-tuned using question-and-answer data sets.

BERT can help computers understand ambiguous texts by executing the following modeling tasks: sentiment analysis, text classification, sentiment analysis, natural language understanding, question answering, and context summarization.

Bidirectional encoder representations from transformers includes three modules on a high level, specifically:

• Embedding: Transform a one-hot encoding vector into an array format. These arrays capture various features of each word, such as its context and overall meaning. In practice, the array of integers assigned to the word “horror” resembles that of the word “scary”.
• Encoders: Convert embeddings into transformer representations by enhancing the embedding layer with positional encodings. These positional encodings indicate the positions of different words within the input sentence.
• Un-embedding: Transform the output from the transformer back into a one-hot vector to facilitate loss calculation.

Bidirectional Encoder Representations from Transformers have contributed to its proficiency in interpreting patterns of more than 70 different languages by December 2019. Despite surpassing other Google’s NLP techniques in supporting voice search and text-based search, Google BERT struggles with machine translation.

Advantages of BERT

BERT’s NLP demonstrates its popularity with various pre-trained models and comprehensive tutorials from researchers and data scientists.

This open-source language model can optimize the interpretation of user search queries to support multiple industries in executing the following tasks:

• Text classification and representation: Effectively compute vector representations for various downstream tasks. It’s also exceptional in understanding language structure and context using a multi-layer bidirectional transformer encoder.

• Data labeling: Assist data scientists in predicting labels for unlabelled data. Indeed, a pre-trained BERT model can generate sentiment analysis labels with a classification layer. Afterward, data scientists are able to apply these labels to train a smaller classification model, which is then deployed within an enterprise pipeline.

• Ranking and recommendation: Support e-commerce businesses in ranking objects and user reviews on Google search results. Indeed, the transformer BERT model can provide natural inputs for ranking and recommendation platforms with high-quality text representations. Meanwhile, Amazon also leveraged the BERT-based system to recommend their products in the marketplace.

• Computational efficiency: The BERT LLM model’s simplicity marks it as an efficient computational solution. While popular NLP models like GPT-4 and Palm 2 require complex GPU systems for fine-tuning and inference, BERT model training can fit on a modern laptop with a single GPU. Moreover, some smaller versions of BERT models, such as DistilBERT and BERT-Base, can operate on embedded phones and devices.

• Accelerated development: BERT models enable faster implementation and deployment by saving time in training, fine-tuning, and compressing models. Moreover, this model only requires a small amount of in-house data and has optimized performance beyond other simpler models.

What Are BERT’s Core Concepts?

Core concepts that form the foundation of Bidirectional Encoder Representations from Transformers’s functionalities include:

• Transformer architecture: This neural network design enables this model to analyze relationships between words in a sentence. Unlike traditional sequential models, transformers can simultaneously handle entire sentences to capture intricate contextual details.
• Bidirectional learning: The BERT model processes text through a bidirectional method. Specifically, it will consider the preceding and following words to analyze a word’s context, providing a more nuanced comprehension of its meaning and intent.
• Pre-training on Masked Language Model (MLM): BERT undergoes pre-training on an extensive text dataset with masked random words. The model predicts these masked words using the surrounding context, generating a deep understanding of word relationships and their functions within language.

How Does The BERT Language Model Work?

Core Architecture and Functionality

While other neural networks use unidirectional or context-free algorithms, BERT models are bidirectional. They consider the previous and following words to generate text predictions. Indeed, this transformer-powered model integrates the self-attention layer into the encoder and decoder to capture the contextual relationships between words in the input sentence.

Google has been developing two versions of pre-trained BERT: BERTbase and BERTlarge. Regarding test accuracy, BERTlarge outperforms the other because it’s created with 24 transformer layers, 16 attention layers, and 340 million parameters.

Pre-training and Fine-tuning

The BERT model undergoes four days of pre-training on Google’s BookCorpus (~800M words) and Wikipedia (~2.5B words). This approach mitigates the need for massive datasets and enables the model to acquire knowledge in multiple languages worldwide. Moreover, Google developed the Tensor Processing Unit for machine learning tasks to optimize the training process.

Besides, Google researchers employed transfer learning techniques to distinguish the pre-training phase from the fine-tuning phase, preventing unnecessary and costly interactions during training. This feature makes the BERT model foundational for diverse applications while granting developers flexible capabilities. Specifically, they can select pre-trained models, fine-tune the input-output pair data for the target task, and retrain the head of the pre-trained model using domain-specific data.

Difference Between BERT and GPT Models

BERT and GPT are two of the earliest pre-trained algorithms that contribute to performing NLP tasks. These two models have differences in data processing methods and ranges of applications.

BERT Use Cases

BERT excels in executing NLP tasks with state-of-the-art results. Powered by transformers, the BERT model can handle the following functions effortlessly:

• Answering questions: Excel as a general question-answering model by understanding the context of a question in the surrounding passage.
• Analyze sentiment: Regarding sentiment and emotion analysis, capture the feeling associated with a given text by comprehending the text’s language.
• Generate text: Demonstrate capabilities in generating long texts from simple prompts as the precursor of next-generation chatbots.
• Summarize text: Read and summarize texts from complicated fields like law and healthcare.
• Translate language: Trained with multi-language data to support translating input prompts for global users.
• Complete tasks automatically: Assist businesses in automating daily routine tasks, such as emails and messaging services.
• Recognize entity name: Identify an entity name mentioned in the text referring to a location, a person, or an organization.
• Classify text: Classify the input text into predefined categories, like spam and not spam. This method reduces noise and can be applied to more specific tasks, such as news category classifications.

Real-World Applications of BERT

The BERT LLM model goes beyond experimental practices with various real-world applications. Many businesses have applied AI solutions incorporating BERT models in different sectors:

• Search Engines: Support search engine platforms in understanding the search intent and enhancing the ranking of search results.
• Chatbots and Virtual Assistants: Provide natural conversation and generate diverse information for users based on their question-answering and sentiment analysis capabilities.
• Machine Translation: Enhance machine translation by considering context, resulting in more natural and human-like translations.
• Content Creation: Help with writing tasks such as suggesting topics, summarizing articles, and optimizing content for SEO purposes.
• Legal Tech: Mitigate legal issues by understanding and reviewing the contract, searching and analyzing documents, and researching legal matters.

Google is a typical real-world example of adopting BERT applications to enhance search results in over 70 languages. This giant search engine platform can rank content and featured snippets based on the user’s intent when integrating the model. Along with the attention mechanism, Google can provide searchers with helpful information based on the attention mechanism.

Limitations of BERT LLM

Despite various advantages in understanding and analyzing the context to recommend relevant content for users, BERT models need help with nuance, context, and logical reasoning.

• Generate profound comprehension: Despite recognizing patterns and creating coherent sentences, bidirectional encoder representations from transformer language models can generate leading and incorrect interpretations. Due to its inability to distinguish between homonyms and ambiguous statements, BERT models struggle to capture the implication and context beyond the information given.
• Lack of common-sense reasoning: BERT lacks the ability to understand fundamental logical reasoning or deduce information from inexplicit sources. This limitation prevents BERT from handling complex tasks that require human background knowledge and reasoning abilities.
• Lack of creativity and originality: It can not create original ideas and concepts despite being able to paraphrase existing information and generate human-like text. The pre-training objective navigates the model to focus on predicting missing words in sentences rather than producing original thoughts.
• Generate discriminatory and unfair outputs: The biased data used for training language models does not guarantee fairness and inclusivity of the generated outputs. Developers must consider different sensitive aspects regarding gender, race, and other attributes to detect and mitigate biases in the training data.
• Lack of transparency and explainability: The complexities of deep learning challenge businesses in interpreting how the model generates the results. Consequently, ensuring the accountable and responsible use of BERT models is difficult. Additionally, the lack of transparency causes less trust and proper intervention when issues arise.
• Lack of adaptability and flexibility: Substantial retraining is required for the BERT model to adapt to new applications or domains. Therefore, applying this model to different contexts and requirements remains less efficient and practical.  

Integrating BERT Seamlessly with Neurond Service

Neurond excels in implementing strategies to help businesses integrate Generative AI models successfully. By understanding the nature of different large language models, like BERT and GPT, for interpreting and generating human-like text, images, audio, and videos, our services can navigate business across various industries to effectively leverage AI technologies.

Contact us now to connect with the best generative AI consultants and scale your businesses with tailored AI solutions.