Do you ever feel like you’re drowning in documents? There’s just too much information to keep track of document Data, Whether you’re working in a small startup or a large transnational corporation. If so, you’re not alone. Document processing can be a time-consuming and tedious task, but it doesn’t have to be.
That’s where large language models (LLMs) come in. LLMs are a type of artificial intelligence (AI) that can be used to automate document processing. They can be used to classify documents, extract information, summarize documents, and even translate documents.\
For example, a bank can utilize LLM and IDP(Intelligent Document Processing) to extract customer information from loan applications, such as income, credit history, and employment details. LLM can then analyze this data, cross-referencing it with predefined criteria and regulations, and provide a risk assessment for each application. This enables banks to make informed decisions swiftly, accelerating the loan approval process.
In this blog article, I’ll explain how LLMs can be used in intelligent document processing. I’ll also provide some examples of how LLMs are being used in different industries.
So if you’re looking for a way to automate your document processing, LLMs are a great option.
Let’s dive into this topic DEEPER.
What are LLMs?
Large Language Models (LLMs) like Google Bard, ChatGPT, and GPT-4 are a type of artificial intelligence (AI) that have been trained on massive datasets of text and code designed to process. This Advance AI Model allows them to understand and generate human-like language, and to perform a variety of tasks that require NLP.
In Easy Language, LLMs are trained using a technique called self-supervised learning. First, the model train oner a large amount of Text, Code, and Image Data Set, then, it is automatically tasked with learning to predict the next word in a sequence and the Image.
Once an LLM has been trained, it can be used for a variety of tasks, including:
- Natural language understanding.
- Image Generation
- Natural language generation.
- Question answering:
- Chatbots
- and many more things.
What is LLM Document Automation?
LLM Document Automation uses large language models (LLMs) to automate document processing tasks. This technology can save money and time by automating document processing tasks.
For example, in Bank or any Financial Institute, they have a huge amount of Paper Account openings, loans, KYC, and many types of Documents. If an employee can manually check and copy-paste data to a digital database, it takes more time and became prone to data. In this case, by using LLM Document Automation and IDP, it is faster, improves accuracy and if there is a data mistake, it can be notified and predict the oriented result.
Industry-wise Benefits of Using LLMs for document processing
1. Do More with Less Data
Its means, LLM Model. Generative pre-trained Models like Google Bard, and GPT-4, can generate legal documents, contracts, financial reports, or medical summaries based on minimal input, saving time and effort for professionals. This can help legal professionals to save time and focus on more strategic tasks.
2. Classifying documents
As we already know, the LLM model can able to understand the human natural language, what we call in real life, like, “How are you?”. “Are you going to the bank? “, “Here is your PAN Card”. With this self-understanding power, this LLM mode can categorize and classify documents based on their content, helping professionals organize and manage large volumes of data.
3. Improving data extraction accuracy
LLMs excel at interpreting context in unstructured datasets, significantly enhancing data extraction precision for complex documents such as financial data, or medical diagnoses. Overall using the combination of IDP and LLM improve the data accuracy.
4. Responding to natural language queries
By providing the LLM with relevant information, it can identify trends, patterns, and insights, transforming raw data into actionable intelligence. LLMs can be used to simplify the search and analysis of critical business documents. First, by using OCR, converting image documents to machine-readable text, and then using the extracted information to train an LLM.
This interface would allow users to ask questions and issue commands in natural language, such as:
- “What is the applicant’s name?”
- “What is the total amount?”
- “What is the check number or payment number?”
- “When does this contract expire?”
This would make it much easier for businesses to find the information they need, and to make better decisions based on that information.
5. Translating documents
Suppose, an organization or institute have a huge amount of customer application which is written local language, Hindi, Bengali, French, and Spanish. TO extract and recognize the data of these types of documents is very complicated for Normal People who don’t know any language. Here, IDP with LLM technology has come. It is trained in different language of different country which help the LLM to identify the key points of a document and to generate accurate summaries.
The future of document processing with LLMs
The future of Intelligent document processing with LLMs promises increased automation, improved accuracy, and more efficient handling of documents across industries. In 2022, ChatGPT and other LLMs captured the public imagination. Though These models are not directly applicable to document processing, in 2023, the latest GPT-4 and LLM models are doing this and it has already been implemented in many organizations. Why are you waiting? LLMs Model is continuously improving the ability to understand and interpret natural language, enabling more sophisticated document processing tasks.
Know More: How IDP Can be used for Business Intelligence
