GPT models have brought a revolution in many areas with their ultra-super natural language processing (NLP) capabilities. Generative Pre-trained Transformers (GPTs) as they are referred to are machine learning models utilized to produce language that is both contextually relevant and semantically consistent. These models are pre-trained on vast amounts of data, including books and web pages.
GPTs are a huge advancement in natural language processing that enable machines to comprehend and produce language with previously unheard-of fluency and accuracy. From GPT 1 to GPT4, these models have begun a new era for NLP applications. These models are based on humungous parameters that constitute their strength. For instance, GPT 3.5 has 175 billion parameters, while GPT 4 is supposed to have a staggering 170 trillion parameters.
Language extraction, text pattern extraction, and data extraction are use cases around data extraction where GPT models prove highly powerful. The intelligent data extraction solution we developed for one of our clients signifies this strength. We will go into the details before which we discuss the applicability of GPT for language extraction and how it surpasses the traditional approaches.
How is GPT offering new dimensions to the language extraction field?
GPT models are based on some of the largest neural networks ever produced which sets them apart when it comes to language extraction. Through generative pre-training, they generate coherent and relevant results.
Using the transformer architecture, GPT models overcome many limitations of previous approaches in language extraction. The Transformer architecture allows for the efficient processing of large amounts of text and enables GPT models to capture long-range dependencies in language, resulting in more accurate, contextually, relevant, and nuanced text generation.
In scenarios where data is present in various layouts and formats, structures are complex and there’s a hierarchical relationship in the text, GPT models prove quite viable as they minimize the need for manual effort in configuring and maintaining extraction rules.
Why using GPT models for data extraction better than traditional extraction?
GPT-based data extraction outperforms traditional document data extraction methods due to its comprehensive training on vast amounts of up-to-date text data, enabling accurate extraction from structured, semi-structured, and unstructured documents.
The models understand context, semantics, and nuances, ensuring accurate data extraction even with complex sentence structures and implicit information.
Unlike traditional methods that require manual rule-based approaches, GPT models generalize well to new document formats, reducing the need for extensive configuration.
Additionally, GPT models can handle multilingual data seamlessly, eliminating the need for language-specific approaches. Continual improvement and fine-tuning ensure that GPT models stay up-to-date, making them a versatile and effective solution for efficient data extraction.
What is the use case?
One of ODA’s clients, a renowned global professional services firm providing solutions in various industries, including finance, healthcare, technology, and more came across this problem. It grappled with multiple technical challenges, such as the absence of a centralized system that made it difficult to search and retrieve specific data points from the documents.
Unraveling the process to develop the solution
To build a comprehensive intelligent dashboard for data extraction and automating the extraction process, we took a series of steps and leveraged various tools such as Open AI’s GPT models, Langchain, Pinecone, and others and built an intelligent dashboard. We will go into the process and help you how we went on to build a cutting-edge solution.
We first brought in OpenAI’s GPT models like OpenAI 3.5-Turbo and Open AI 4 to perform language processing tasks. These models were fine-tuned on a large amount of data, which set the course for accurately understanding and extracting relevant information from the uploaded documents.
Here, we chained together different components using LangChain, a framework built around Large Language Models (LLMs). The chains consisted of multiple components from several modules such as Prompt Templates, Large Language Models (LLMs), Agents, and Memory.
Data Extraction and Embedding
The developed solution could automatically extract structured and unstructured data from PDF and Excel files. The extracted data was then transformed into embeddings - numerical representations of the documents' content. These embeddings captured the semantic meaning of the documents and facilitated efficient search and retrieval.
Storing document embeddings
Next, we used Pinecone’s indexing and search capabilities to rapidly and accurately retrieve documents against user queries. Using Pinecone as the vector database helped in efficiently matching the query embedding to the document embeddings, providing relevant results in real-time.
Through this process, we embedded two important features into the solution viz. Trend Analysis and Benchmarking. Let’s explore more about their work and functionalities.
Here, the objective was to ease the financial data analysis process for the client/user. The client has to upload a financial databook file that consists of different performance KPIs, and by using a drop-down option the user can visualize the data with different types of plots.
The user gets answers to the questions surrounding different performance KPIs and can generate a summary of how a particular company has performed over previous years and what has led to the growth or decline of a KPI.
Here is a pictorial representation of the workflow highlighting the working of Trend Analysis:
This feature allows users to benchmark a company with respect to the industry and its peers. The user just has to select the KPIs and the peers against which the company should be benchmarked. Based on the selected KPIs, all the corresponding values for each KPI would be retrieved from the web using a search agent and the output would be formatted in a tabular format for ease of comparing the KPIs for a company and its peers.
The following workflow sheds light on how the feature works:
What were the benefits of the solution?
The intelligent dashboard we conceptualized and developed fetched several benefits, which were beyond the expectations of the customers. Overall, the offering was crucial in terms of optimizing the entire process through:
Automated Data Extraction
The solution eliminates the need for manual data extraction, saving time and resources. It ensures accurate and efficient extraction of information from PDF and Excel files, improving productivity.
With the integration of OpenAI GPT models and Langchain, the dashboard can understand user queries in context and provide accurate answers based on the uploaded documents. This enhances decision-making and accelerates data-driven insights.
Efficient Document Search
By storing document embeddings in Pinecone, the system enables quick and precise search of specific information within the document repositories. Users can easily retrieve relevant documents based on their queries, enhancing knowledge retrieval and analysis.
Bringing Scalability and Flexibility
The solution is designed to be scalable and adaptable to different business use cases. As A&M's document repositories grow, the system can handle the increasing volume of documents while maintaining high performance and accuracy.
Large language models (LLMs) like GPT-3.5 and GPT-4 can prove quite useful in intelligent document data extraction. The value of these techniques becomes evident when the context is of utmost importance.
The solutions we discussed here justify the efficacy of the GPT models and how using them in conjunction with other tools can seamlessly streamline data extraction processes.
The project is still in progress, as we plan to integrate many more features that would increase the efficiency of the solution, and in turn the efficiency of the clients.