Since OpenAI released ChatGPT in 2022, most people in almost every industry have tried the generative AI tool at least once. The market size for Generative AI is expected to show a CAGR of 24.40%, resulting in a market size of USD 207 billion by 2030. The technology can be beneficial in a number of ways. One such is extracting data from documents using OpenAI.
Read this post to discover the applications and use cases of ChatGPT-based AI for data extraction from documents, the challenges and limitations of the technology, and its prospects.
How OpenAI GPT can help extract data from documents?
OpenAI’s ChatGPT is a large-scale language model (LLM) designed to understand and generate human text based on the input it receives. The technology uses large-scale ML and natural language processing (NLP) which enables it to provide an answer to a data extraction question based on a specific query.
Among the top large language models, ChatGPT stands out for its advanced data extraction capabilities from documents. Let’s start by reviewing OpenAI GPT applications in this area. This list of possible uses of technology includes, but is not limited to:
- Contextual understanding: Understanding the context in which words or phrases are used. This capability is essential for tasks such as sentiment analysis, machine translation, and dialog systems.
- Automatic replies: Extracting and interpreting customer queries from email or text support channels to provide automated but accurate responses. It is also useful in knowledge management, where automated FAQs can be generated or updated.
- Summary of the text: Generating concise summaries of long documents, reports, or articles to aid in quick decision-making and information dissemination.
- Named Entity Recognition (NER): Identifying and classifying named entities such as names of people, organizations, locations, time expressions, quantities, and more. This is important for information retrieval, data mining, and customer service bots.
- Answer to the question: Receiving questions and giving accurate and concise answers. This can be applied in domains such as customer service or academic research.
- Account processing: Extraction of relevant financial data from invoices for automated entry into accounting systems.
- Management of medical documentation: Extracting and summarizing critical information from health records for easier access and interpretation by health professionals.
- Market research: Analyzing newspaper articles, reports and other documents and extracting data such as market trends, customer preferences or competitive intelligence.
- Continue projection: Screen resumes to extract educational background, skills, experience and other relevant information for automated initial screening.
Using artificial intelligence to extract data from documents can be helpful in many ways, depending on the specific needs of businesses in different sectors.
Examples of successful use of OpenAI GPT in a data extraction task
Despite the fact that generative AI technology became openly available not so long ago, it is already being used extensively. Here are some real-world examples of AI-powered document extraction, along with other generative examples of AI usage that demonstrate the growing popularity of the technology in the business environment:
A sustainable platform for generative analysis
The sustainable platform enables businesses to better handle customer support tickets and gain actionable insights from customer interactions to improve their Net Promoter Score (NPS).
They have begun to exploit the capabilities of OpenAI’s fine-tuned LLMs to analyze qualitative data at a level beyond conventional techniques. In this way, they can help their clients make sense of the vast amounts of data they generate by interacting with users. Viable users claim that the generative analysis feature saves them nearly 1,000 hours a year.
Yabble platform for analyzing feedback
The Yabble platform enables companies to extract data from customer feedback to inform their business strategies and save time on manual data processing.
Yabble Count, an AI tool powered by OpenAI ChatGPT, can analyze thousands of comments and other unstructured data sets, categorize them by sentiment, and organize the data into topics and subtopics. Ben Roe, product manager at Yabble, says, “Users loved how easy it was to finally make sense of mountains of data and feedback forms and present that information in a digestible way.”
Development of a B2B platform for finding jobs
The challenge was to ensure high-quality analysis of job descriptions and matching of candidate profiles to job requirements. This would help the client simplify finding candidates on the platform. As an additional condition, the solution should comply with the principles of diversity, equity and inclusion (DEI).
The solution was an ML model driven by NLP technology created by the Intelliarts team. It can compare candidate profiles from classifieds sites or social media such as LinkedIn with positions companies are looking to fill. This is done by analyzing textual descriptions and extracting and combining key phrases. The solution includes a semantic search engine that supports multiple search filters, such as age, gender, race, etc. and shows more than 90% accuracy for gender and ethnicity detection.
It is worth noting that generative artificial intelligence is not the only technology that can perform data extraction tasks. You can also use document extraction, a non-generative AI designed to extract specific information from documents, or rule-based document extraction software.
The detailed use cases are just a few of the many examples of adopted data extraction with ChatGPT as companies usually do not disclose information about such things. The range of industries and businesses that operate within them and make extensive use of ChatGPT’s data extraction is shown in the infographic below.
Challenges and limitations of GPT-based document data extraction
As with any technology, the use of artificial intelligence to extract data from documents is not without complexities that you should be aware of. Here is a list of the main challenges of extracting document data using ChatGPT:
- Ambiguity and contextual errors: Although GPT is good at general language tasks, it can misinterpret ambiguous terms, resulting in GPT not always recognizing the correct meaning based on context.
- Difficulties with numerical data and visual elements: GPT models are primarily text-based. Thus, trying to extract statistical or mathematical data, as well as analyzing complex document structures such as tables, spreadsheets or forms, may not be error-free. This also applies to cases of working with PDFs that include images, diagrams or graphs. For them, you will need additional tools that support OCR (Optical Character Recognition) and image recognition.
- Legal and ethical issues: If you are extracting sensitive or personal data, GPT does not provide any built-in privacy protections. This presents risks in terms of data security and you may face non-compliance with regulations such as HIPAA or GDPR.
- Lack of accuracy and consistency: GPT can be inconsistent in its responses, even to the same questions about the same documents. Thus, it requires verification steps to ensure the reliability of the data.
- Lack of domain knowledge: This mainly applies to general-purpose GPT LLMs since specialized models are usually well trained on domain-specific data. So it’s worth understanding that the general model may not understand jargon or complex terminology.
- Token limit: Each GPT model has a maximum token limit, which typically ranges from a few hundred to a few thousand tokens. This limits the amount of text you can process in one go, complicating extraction from longer documents.
Using document text extraction with ChatGPT can be recommended. However, it is worth considering that the technology is not specifically designed for this task. Thus, such solutions need adaptation and probably the use of additional instruments to become highly effective.
There are ways in which the above challenges can be solved through the custom development of artificial intelligence. For example, a provider of such services can use a multimodal approach, combining the advantages of different artificial intelligence algorithms. Another opportunity is to add validation layers that check the accuracy and quality of the ChatGPT model’s responses.
The future and prospects of extracting data from documents via OpenAI GPT
It is possible to foresee the growing use of data extraction using AI ChatGPT technology. The reason is that it can potentially develop in the following ways:
- Improved structure recognition: Future iterations could be fine-tuned to better understand structured data like tables, patterns, or even coded languages, making GPT models more versatile in document extraction tasks.
- Ethical and legal protections: As AI ethics and regulations mature, built-in data privacy features and compliance checks could become standard, mitigating legal and ethical concerns.
- Integrated multimodal possibilities: Next-generation versions could potentially integrate with OCR and image recognition technologies to handle mixed-media documents, making them more comprehensive in their extraction capabilities.
- Debugging and Validation: Advanced validation algorithms could be incorporated, either as part of GPT or as a complementary system, to automatically verify the accuracy of extracted data.
- Update and learn in real time: If future versions can be updated in real-time or even adjusted on the fly, they could offer more up-to-date and context-sensitive data extractions, solving the problem of knowledge discontinuity.
- Improved scalability: Advances in hardware and optimization algorithms could potentially solve token limitations, allowing longer documents to be efficiently processed in one go.
- Collaborative AI systems: GPT models can work in tandem with other specialized AI systems for even more efficient and nuanced data extraction tasks.
When it comes to data extraction using artificial intelligence, despite the limitations of the technology as of 2023, it can improve significantly over the next decade. Thus, the adoption of generative artificial intelligence today is the first step to take full advantage of the advanced technology in the near future.
Final Take
Using ChatGPT AI to extract data from documents has proven useful for various companies and is becoming more widespread. Technology can help create short summaries, highlight key information, and more. However, it is worth keeping in mind the challenges and limitations of the technology such as lack of consistency, difficulties with numerical data, etc. In any case, the future of document analysis with ChatGPT seems promising.