AI is rapidly changing how we approach work. Large language models (LLMs) garner a great deal of attention because they provide a foundation for applying AI to business processes. Numerous off-the-shelf tools are helping users summarize information, perform research, or extract salient details from large documents or bodies of unstructured information.
But to gain a complete and accurate understanding of your business data, you need more than standalone LLMs. As mentioned in part one and two of our Overcoming the Limitations of LLMs series, you need mechanisms for finding and assembling structured and unstructured data within a document and then chunking and storing it for rapid retrieval. However, there is more to consider if you want a solution that delivers accurate results that provide business value — such as enhancing operational efficiency and decision-making.
Exploring the Hallucination Challenge
You must also establish mechanisms for preventing so-called “hallucinations,” which occur when AI models generate information that is inaccurate, misleading, or entirely fabricated, despite the appearance of confidence and authority.
Hallucinations in LLMs can have far-reaching consequences for organizations that rely on AI for document processing and analysis. When a model inaccurately interprets document content, it may mislead decision-makers and potentially compromise strategic outcomes. In industries where precision and reliability are critical — such as finance, legal, and healthcare — the stakes can be especially high. Accurate document understanding solutions are not a mere technological luxury; they are essential to well-informed decisions, risk mitigation, and competitive advantage.
Grounding Answers With References
Instabase has adopted a holistic approach to document understanding. LLMs obviously play a critical role in that, but Instabase AI Hub adds agent-based retrieval, with sophisticated digitization, parsing, and representation. It also focuses on preventing hallucinations using advanced techniques for chunking and retrieving data.
Most people who have studied LLMs will be at least somewhat familiar with Retrieval-Augmented Generation (RAG), a technique that combines information retrieval, prioritization, and the formulation of a response. RAG operates in two distinct phases:
- Information Retrieval: The system first employs a retrieval mechanism to search through a vast database of relevant documents or other sources, then identifies and extracts the most pertinent information related to the query at hand.
- Response Generation: Once the relevant information has been retrieved, a generation model processes this data to formulate a coherent and contextually appropriate response.
RAG has become a de facto standard for delivering the appropriate amount of relevant, contextual information in response to a prompt. Yet if the RAG pattern is not deployed with care, LLMs can still deliver results that contain hallucinations.
To prevent that, you must keep LLMs grounded in the proper context. Responses should draw from a document’s context, rather than relying on their inherent knowledge representation. It is useful to apply structured prompts that explicitly call for responses grounded in the source document of interest. When asking for a summary of commercial terms within a contract, for example, a structured prompt might offer the full text of the contract, the request for a summary of commercial terms, and explicit instructions that the response should be limited to the facts of this particular contract.
AI Hub carries this a step further by maintaining references back to original documents or chunks within a document. This is akin to a footnote, offering a means for tracing responses back to their original sources, and thereby adding integrity and traceability to LLM outputs.
Within AI Hub, this can occur on two levels:
- Document/chunk-level references indicate the document or document chunks that contributed to an answer, linking to a complete source document or section thereof.
- Word/phrase-level references provide a more granular approach that’s suitable for use cases in which it is critical to tie responses back to the exact phrase contained in the source document.
Developing and Using Confidence Scores
Several sources of uncertainty can impact the accuracy and reliability of the information generated by a document understanding system.
Document quality is affected by factors such as scan resolution, text legibility, and formatting. Ambiguities, errors, typos, or inconsistencies can introduce further uncertainty into the data interpretation process.
Model confidence describes the level of certainty the model has in its response. Since document understanding systems generate probabilistic outputs, confidence scores can enhance overall accuracy by measuring the reliability of each prediction and setting thresholds for human intervention. Their confidence level can vary, depending on the clarity of the source data and the complexity of the query, for example.
Unlike other technologies used in document understanding, LLMs do not natively generate confidence scores. If you are using LLMs for document understanding, you must consider that and create a way to generate your own confidence scores.
To address these challenges, AI Hub has been designed to calculate confidence scores based on a combination of factors, including optical character recognition (OCR) confidence, prompting, and log probabilities. In aggregate, these scores provide measures of confidence in a given prediction and help to prioritize cases in need of human verification. This enhances the accuracy of document processing workflows and reduces the risk of acting on erroneous information.
Instabase AI Hub for Hallucination-Free Data With Enhanced Integrity
LLMs are a transformative technology that is already delivering outstanding improvements to productivity and effectiveness. Yet LLMs alone are not sufficient for automating business processes and informing strategic decisions.
Instabase AI Hub is designed to unlock the full potential of LLMs within a broader framework that includes tools for preventing hallucinations and verifying data integrity. By combining intelligent deployment of agent-based RAG with source document references and confidence scores, AI Hub delivers deep document understanding without the risk and uncertainty of the standalone LLM approach. Sign up for a free account to explore how Instabase AI Hub can address all of your document understanding needs, or request a personalized demo.
Part four of this series will discuss the need for data validation and human review when using LLMs for document understanding. To gain an understanding of how to overcome the limitations of LLMs and gain deeper insights into your documents, download the whitepaper “LLMs Are Not All You Need: Full Stack Document Understanding with Instabase AI Hub.”
