Automating Data Entry with LLM: A Game Changer

Developments in the field of Language models has resulted in some tremendously useful API’s by OpenAI and other companies to provide users their models to build LLM applications. Lang Chain is a framework that helps to use these models in an organised and hygienic manner. In this article we talk about how data from documents can be extracted in structured format , this helps in automating the tasks of data entry and can help in making data validation faster.

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Why Automate Data Entry?

Manually entering data from various documents is time-consuming and prone to errors. Automating this process not only saves time but also ensures that the data is accurate and consistent. This can be incredibly beneficial for teams in customer service, marketing, and other departments that rely heavily on data to make informed decisions.This also helps in improving quality of real time data analysis.

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Extracting Data from Documents

The process of extracting data from documents involves a few key steps:

• Input Document: Start with a document, which could be a PDF, a web page, or any other text file.
• Define Schema: Specify the required fields, their data types, and descriptions to outline what data needs to be extracted.
• Initialise the Model: Use a language model like GPT-4 to create an extraction workflow.
• Data Processing: Break down the document into manageable chunks for easier processing by the model.
• Extracted Data: The model then extracts the data according to the defined schema and stores it in a structured format.

By transforming unstructured documents into structured data, teams can quickly access the information they need to make better decisions and communicate more effectively.

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Validating Data with QA (Question-Answering)

To ensure the extracted data is accurate, we use a method called Question-Answering with Retrieval Augmented Generation (QA with RAG). Here’s how it works:

• Generate Questions: Create a set of questions to validate the extracted data.
• Embed Text: Convert the document content into embeddings and store them in a vector database.
• Answering Chain: Use the model to answer the questions based on the document content.
• Validation: Compare the extracted data with the answers generated by the QA process.

This step ensures that any missing or unclear information from the initial extraction can be verified and corrected.

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Combining Results

To further refine the data, we compare the outputs from both the structured extraction and the QA process. We create a table with the following columns:

• Key: The attribute we want to extract.
• LLM1: Values from the structured data extraction.
• LLM2: Values from the QA process.
• Mixed: Fill in any gaps from the structured extraction with data from the QA process.

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Summarizing Documents

Additionally, we generate a summary of the document using the LangChain framework, which helps quickly understand the content of the documents. This summary provides a concise overview, making it easier to digest large amounts of information and also for a quick understanding by any user of the document.

Automating data entry and validation using LLM not only enhances efficiency but also ensures data accuracy. By leveraging advanced language models and frameworks, businesses can streamline their processes, reduce manual labor, and make more informed decisions. This change can be observed in almost every sector where manual data entry is required. 

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Business Impact of Automating Data Entry:

A Zapier survey found that 76% of employees spend up to 3 hours daily on data entry. Organisations using automated data entry software report a 95% reduction in this time. According to a 2020 McKinsey survey, automation cuts costs by 10-15%. Financial data errors alone can cost over $800,000 annually in rework, as per Gartner. However, businesses that automate have seen a 90% reduction in manual invoice processing time, showcasing significant returns on investment.

Embrace the power of automation and transform how your organisation handles data entry and validation today!

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