These are not your grandmother’s models: the impact of LLM’s on Document Processing

u003ch2u003eDocument Processing before LLMsu003c/h2u003ernu003ca href=u0022https://en.wikipedia.org/wiki/Document_processingu0022 target=u0022_blanku0022 rel=u0022noopeneru0022u003eDocument processingu003c/au003e primarily relies on rule-based systems and keyword matching, which can be effective for structured or even semi-structured documents with predictable formats. However, this approach often struggles with unstructured data, where variability and complexity are high. In contrast, Large Language Models (u003ca href=u0022https://en.wikipedia.org/wiki/Large_language_modelu0022 target=u0022_blanku0022 rel=u0022noopeneru0022u003eLLMsu003c/au003e) bring a transformative approach to document understanding. They leverage advanced natural language processing (u003ca href=u0022https://en.wikipedia.org/wiki/Natural_language_processingu0022 target=u0022_blanku0022 rel=u0022noopeneru0022u003eNLPu003c/au003e) techniques, enabling them to comprehend context, semantics, and nuanced language variations in documents.rnrnIn the ever-evolving world of data science and enterprise automation, the explosive growth of unstructured data generated by companies has been a major challenge for data scientists. To give you a sense of scale, recent studies show we’re likely to u003cstrongu003eu003cemu003ewitness a surge from 33 zettabytes in 2018 to a predicted 175 zettabytes by 2025u003c/emu003eu003c/strongu003e. Furthermore, according to Gartner, u003cemu003eu003cstrongu003eunstructured data currently represents an estimated 80 to 90 percent of all new enterprise data. u003c/strongu003eu003c/emu003e Unstructured data can include conversations through e-mail or text messages, but also social media posts, blogs, video, audio, call logs, reviews, customer feedback, and replies in questionnaires. This trend spotlights an urgent need for more sophisticated tools to u003cstrongu003eu003cemu003ecreate valueu003c/emu003eu003c/strongu003e from this burgeoning data deluge.rnrnOur team has over 5 years working with various OCR and NLP technologies, including having developed and training models in-house.  Don’t get me wrong, IDP  tech has come an extremely long way and the tools have gotten tremendously powerful. Libraries such as u003ca href=u0022https://aws.amazon.com/textract/u0022 target=u0022_blanku0022 rel=u0022noopeneru0022u003eAmazon Textractu003c/au003e (among many others) provide ML engineers a powerful suite of tools to accelerate the speed and quality of applying intelligent document processing to automation scenarios.rnrnHowever, there are still limitations to how IDP can be adopted to a range of automation scenarios that we encounter in enterprise environments.rnrnThink of traditional models document processing tech as a diligent yet somewhat myopic librarian, meticulously following rules but often missing the bigger picture. In contrast, Large Language Models (LLMs) are like Sherlock Holmes — insightful, context-aware, omnipresent, and adept at deciphering the most cryptic of texts.rnrnThis results in several key benefits and improvements:rnu003ch3u003eEnhanced Comprehensionu003c/h3u003ernu003cstrongu003eTraditional Method:u003c/strongu003e Typically relies on keyword spotting and pattern recognition. For example, extracting dates or specific terms from structured forms.rnrnu003cstrongu003eLLMs Approach:u003c/strongu003e Goes beyond mere pattern recognition. It interprets language nuances and intent, essential in contexts like financial and legal document analysis where the meaning of clauses and data can be complex.rnu003ch3u003eFlexibility with Unstructured Datau003c/h3u003ernu003cstrongu003eTraditional Method:u003c/strongu003e Struggles with documents like unstructured emails or reports, often leading to high error rates or the need for manual intervention.rnrnu003cstrongu003eLLMs Approach:u003c/strongu003e Excel in handling unstructured formats. For instance, in customer service, LLMs can analyze and respond to diverse customer queries that vary in structure and content, easily extract information from employment letters or mortgage commitment statements.rnrnDealing with unstructured data, which includes everything from casual emails to social media chatter, videos, and customer feedback, is not a trivial matter. This kind of data resists neat categorization and defies traditional database structures, posing significant challenges in analysis and comprehension. Here’s where Large Language Models show their mettle, adeptly navigating this complex, non-uniform data and unlocking valuable insights that conventional methods might miss.rnu003ch3u003eAdaptive Learningu003c/h3u003ernu003cstrongu003eTraditional Method:u003c/strongu003e Updating rule-based systems for new formats or languages is time-consuming and resource-intensive.rnrnu003cstrongu003eLLMs Approach:u003c/strongu003e Can continuously learn from new data, adapting to changes in language usage or document formats without extensive manual reprogramming.rnu003ch3u003eError Reductionu003c/h3u003ernu003cstrongu003eTraditional Method:u003c/strongu003e Prone to errors in cases of ambiguous or context-heavy information, resulting in lower reliability.rnrnu003cstrongu003eLLMs Approach:u003c/strongu003e Their deep contextual understanding leads to more accurate data extraction and interpretation, crucial in high-stakes industries like legal, financial and healthcare.rnu003ch2u003eA Practical Exampleu003c/h2u003ernChatGPT 4, without any specific fine tuning or pre-training is able to easily extract information from a document it has never seen before. It understands the context and you can simply query in a natural way for data points that you are interested in:rnrn*u003cimg class=u0022alignnone wp-image-7105 size-fullu0022 src=u0022https://blanclabs.com/wp-content/uploads/2024/01/Chat-gpt-extracts-information_Blanc-Labs.webpu0022 alt=u0022Chat gpt extracts information_Blanc Labsu0022 width=u0022720u0022 height=u0022884u0022 /u003ernrnu003cemu003eu003cspan style=u0022font-size: 10pt;u0022u003e* Note: we take data privacy and PII seriously (see below) and created u0022spoofu0022 documents for the purposes of this demonstration.u003c/spanu003eu003c/emu003ernrnFor instance, by asking direct questions such as ‘What is the policy end date?’ or ‘By what margin has the insured amount varied?’, it promptly delivers precise information with perfect accuracy.rnrnu003cimg class=u0022alignnone wp-image-7106 size-fullu0022 src=u0022https://blanclabs.com/wp-content/uploads/2024/01/Chat-gpt-simple-query_Blanc-Labs.webpu0022 alt=u0022u0022 width=u0022720u0022 height=u0022276u0022 /u003ernrnThis scenario offers the opportunity to further explore solutions that leverage the unique capabilities of LLM’s for the purposes of intelligent document processing and automation.rnu003ch3u003eGenerative AI-Powered Extraction and Comparison of Insurance Policy Documentsu003c/h3u003ernIn the previous section, we delved into the capabilities of Language Learning Models (LLMs) in streamlining the extraction of key information from various document types. To demonstrate this further, we now present a practical application of our system.rnrnThe first part of the demonstration involves uploading the initial insurance policy, which acts as our benchmark document. Watch how the system seamlessly processes this document, effortlessly extracting critical details such as the policy number, coverage specifics, the insured party’s information, and other essential data.rnrnNext, we upload a second document representing a modification in the policy. It not only extracts pertinent information from the new document but also conducts an intelligent comparison with the original policy. Notice how the system highlights the changes in the date and insurance limit. This comparative analysis is vital to ensure comprehensive and accurate updates of all modifications and their implications.rnrnu003ciframe title=u0022YouTube video playeru0022 src=u0022//www.youtube.com/embed/vyTfyNoLcTE?si=zvr0I3C46YCPpa93u0022 width=u0022560u0022 height=u0022315u0022 frameborder=u00220u0022 allowfullscreen=u0022allowfullscreenu0022u003eu003c/iframeu003ernrnTo enhance the efficiency of such systems, integration with existing databases and cloud storage services is key. Utilizing APIs, these systems can automatically retrieve documents from various sources such as cloud storage (like AWS S3, Google Cloud Storage), internal databases, or even directly from email attachments. This integration enables real-time processing and updates, ensuring that the latest documents are always analyzed and compared.rnu003ch3u003eThe Role of Retrieval-Augmented Generation (RAG) in LLMsu003c/h3u003ernFor more context specific answers and solutions, Retrieval-Augmented Generation represents a significant advancement in the capabilities of LLMs. It’s another step forward on this never-ending roller coaster!rnu003culu003ern tu003cliu003eu003cstrongu003eEnhanced Accuracy and Relevance:u003c/strongu003e RAG combines the generative power of LLMs with information retrieval, pulling in relevant data or documents to provide contextually accurate responses. This is particularly beneficial for financial analysis and reporting, where accuracy is paramount.u003c/liu003ern tu003cliu003eu003cstrongu003eDynamic Data Integration:u003c/strongu003e Unlike traditional LLMs, RAG can integrate real-time data, offering dynamic responses to financial queries. This is essential in finance, where market conditions and regulatory environments are constantly evolving.u003c/liu003ern tu003cliu003eu003cstrongu003eCustomized Financial Advice:u003c/strongu003e RAG’s ability to retrieve and process vast amounts of data allows for highly personalized financial advice, tailored to individual customer profiles and market conditions.u003c/liu003ern tu003cliu003eu003cstrongu003eImproved Compliance and Risk Management:u003c/strongu003e In the regulatory-heavy landscape of financial services and healthcare industries, RAG can efficiently process and cross-reference internal and external data sources including detailed regulations and requirements. We believe that there is a huge opportunity to automate regulatory, risk, and compliance checklists to reduce complex manual efforts that exist in regulated industries.u003c/liu003ernu003c/ulu003ernu003ch3u003ePotential limitations of deploying LLM’s for document processing (at scale)u003c/h3u003ernWhile LLMs are highly likely to revolutionize document processing with,  it is important to consider potential limitations as well. LLMs are like a double-edged sword, powerful in processing vast amounts of data but requiring careful handling to address privacy concerns and manage computing resources.rnu003culu003ern tu003cliu003eu003cstrongu003ePrivacy and Personal Identifiable Information (PII):u003c/strongu003e LLMs are capable of processing vast amounts of data, including confidential or proprietary information as well as PII data.  Organizations must work within data security frameworks and engineer solutions that have data security at the core of how they are designed and deployed.  As a u003ca href=u0022https://blanclabs.com/insights/blanc-labs-soc-2-type-2u0022u003eSOC2 Certifiedu003c/au003e company, this is an area of key focus for our teams and we have implemented robust data handling and processing protocols to ensure that all PII is managed securely and in compliance with privacy regulations.u003c/liu003ern tu003cliu003eu003cstrongu003eComputing Power and Cost:u003c/strongu003e The potential impact offered by powerful LLMs to read and extract data from large volumes of unstructured data is reliant on the on substantial computing power required to run these models.  We are still in the early stages of enterprise adoption of generative AI technologies and the economics of leveraging these toolsets at an enterprise scale are fairly dynamic.  We expect a lot to change over the next few years but in the meantime, we are actively working with clients to understand the business drivers of using LLM’s to automate processes.  With our deep background in intelligent document processing we’ve become experts at crafting solutions that optimize model efficiency without compromising performance. We employ techniques like model pruning, efficient data processing pipelines, and cloud-based solutions that balance computational demands with cost-effectiveness.u003c/liu003ernu003c/ulu003ernu003ch2u003eConclusionu003c/h2u003ernLLMs and RAG are the vibrant threads bringing new patterns of efficiency, accuracy, and innovation. We’ve journeyed from the meticulous yet narrow pathways of traditional methods to the expansive highways of AI-driven solutions. This evolution isn’t just a step forward; it’s a quantum leap into a future where data isn’t just processed but understood, where advice isn’t just given but tailored, and where compliance isn’t just followed but mastered.rnrnThe advancements in unstructured data analytics signal a critical shift in our approach to data. It’s not just about the volume; it’s about the untapped potential that lies within. This raises a compelling question: how can we leverage unstructured data to gain a deeper understanding of our customers, societal trends, and the world at large? The key lies in harmonizing cutting-edge AI tools like Large Language Models with human insight, transforming this wave of data into insightful and actionable knowledge.

References:rnu003cul class=u0022u0022u003ern tu003cli id=u0022d4aau0022 class=u0022lp lq fv lr b ls oi lu lv lw oj ly lz ma ok mc md me ol mg mh mi om mk ml mm mn mo mp bju0022 data-selectable-paragraph=u0022u0022u003eu003ca href=u0022https://powerautomate.microsoft.com/en-us/intelligent-document-processing/u0022 target=u0022_blanku0022 rel=u0022noopeneru0022u003e“What is intelligent document processingu003c/au003e?” Microsoftu003c/liu003ern tu003cli id=u0022b6b6u0022 class=u0022lp lq fv lr b ls pe lu lv lw pf ly lz ma pg mc md me ph mg mh mi pi mk ml mm mn mo mp bju0022 data-selectable-paragraph=u0022u0022u003e“u003ca href=u0022https://openai.com/research/gpt-4u0022 target=u0022_blanku0022 rel=u0022noopeneru0022u003eOpenAI Researchu003c/au003e”. OpenAIu003c/liu003ern tu003cli id=u00228461u0022 class=u0022lp lq fv lr b ls pe lu lv lw pf ly lz ma pg mc md me ph mg mh mi pi mk ml mm mn mo mp bju0022 data-selectable-paragraph=u0022u0022u003e“u003ca href=u0022https://aws.amazon.com/what-is/retrieval-augmented-generation/u0022 target=u0022_blanku0022 rel=u0022noopeneru0022u003eWhat is Retrieval-Augmented Generationu003c/au003e?” Amazon.u003c/liu003ern tu003cli id=u0022f532u0022 class=u0022lp lq fv lr b ls pe lu lv lw pf ly lz ma pg mc md me ph mg mh mi pi mk ml mm mn mo mp bju0022 data-selectable-paragraph=u0022u0022u003e“u003ca href=u0022https://research.ibm.com/blog/retrieval-augmented-generation-RAGu0022 target=u0022_blanku0022 rel=u0022noopeneru0022u003eRetrieval-Augmented Generation for Knowledge-Intensive NLP Tasksu003c/au003e.” IBM.u003c/liu003ernu003c/ulu003e