Learn about how Large Language Models like GPT-4 can elevate Intelligent Document Processing and automate manual tasks with highe
In recent years, the rise of large language models like ChatGPT has caused a major shift in the field of natural language processing (NLP). These models have the ability to generate human-like responses to text inputs, making them an incredibly powerful tool for a wide range of applications.
Recently, ChatGPT made headlines when it gained 1 million users in under a week, underscoring just how quickly these models are being adopted by the general public. Moreover, tech giants like Microsoft are investing heavily in the development of these models, with a recent $10 billion investment in OpenAI, the makers of ChatGPT. Google also revealed Bard, which is their own version of conversational AI chat service, shortly after the announcement of ChatGPT.
One area where these large language models have the potential to make a significant impact is in intelligent document processing. By leveraging the power of models like ChatGPT, many manual and error-prone tasks involved in document processing, which include but aren’t limited to classification, data extraction, and validation, can be easily automated with higher accuracy and efficiency.
In this blog post, we will explore the rise of large language models like GPT-4, the latest multimodal large language model developed by OpenAI, and discuss the potential benefits of integrating these models with intelligent document processing solution. We will also examine some real-world use cases where this technology is already being applied to great effect.
Large Language Models (LLMs) like ChatGPT and GPT-4 are designed to process and understand natural language at a level that was previously thought impossible for machines. These models are created by training complex neural networks on massive datasets of text, allowing them to generate coherent responses to a wide range of inputs.
LLMs like ChatGPT and GPT-4 are capable of performing a wide variety of language tasks, including language translation, sentiment analysis, creative writing, text completion, and more. These models are also capable of understanding context and nuance in language, which allows them to generate responses that are tailored to specific situations. How exactly can these models enhance intelligent document processing then?
Intelligent Document Processing (IDP) solutions have already taken data extraction to a new level as the traditional rule-based data extraction tools can only extract data from structured documents. IDP, on the other hand, leverages OCR and machine learning and is capable of extracting data from documents with dynamic layouts and the accuracy can be improved by feeding the extractors with more samples. To make it even more powerful and efficient, we’ve explored bringing GPT-4 into FormX and seen some positive results.
One of the most significant advantages of LLMs in IDP is improved data extraction accuracy. By leveraging the advanced language processing capabilities of LLMs, IDP solutions can better understand the context and meaning of words and this allows them to fix common OCR errors and extract entities more accurately.
This can have significant implications for industries that rely heavily on document processing, such as finance, healthcare, and legal services, where accuracy is critical.
Another way that LLMs can improve IDP solutions is through better document classification. LLMs are capable of understanding context and nuance in language, which makes them ideal for identifying and categorizing different types of documents, which is an essential step of IDP since it will have to first classify the document before selecting the corresponding extractor to extract data. This can further reduce the need for manual intervention and improve efficiency.
Additionally, LLMs can be used to create new custom extractors much faster with fewer training samples. Traditionally, creating custom extractors requires a certain amount of time and samples to train the model.
Traditionally, creating custom extractors can take some time and it usually requires users to submit from fifteen to hundreds of sample documents for training. However, LLMs like GPT-4 have already been trained with massive datasets of text, allowing us to train new extractors with just three to five samples.
Although integrating Intelligent Document Processing with Large Language Models like GPT-4 is still in its early stages, the potential for future development is enormous. Our team have launched a private beta to integrate them and seen promising results that can revolutionize document processing and data extraction.
If you’re interested in seeing how FormX & GPT-4 can take your intelligent workflow to the next level, contact us and tell us more about your business.
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