Overcoming the Limitations of LLMs: Advanced Content Retrieval and Reasoning

Modern business runs on data — often found in a variety of unstructured documents. Effectively managing and understanding vast volumes of documents is a significant challenge. Large language models (LLMs) such as ChatGPT have made strides in natural language processing, but they often fall short in handling complex document structures. 

As mentioned in part one of the Overcoming the Limitations of LLMs series, the first steps on the journey to document understanding are digitizing, parsing, and representing documents accurately.

In this post, we’ll discuss the challenges of using LLMs for content reasoning and how advanced implementations of retrieval-augmented generation and AI agents can address these concerns.

Solving Context Window Challenges in LLMs

LLMs are powerful, and growing more powerful quickly. However, these models face limitations when tasked with document understanding, especially when dealing with context windows. The impracticality and expense of passing extensive data sets to LLMs with every call often result in inefficiencies. LLMs struggle to utilize such large data sets effectively, leading to issues in extracting meaningful insights from complex documents.

What can be done to address these issues?

Retrieval-Augmented Generation (RAG)

RAG has emerged as the standard for providing LLMs with the right amount of context. By combining data sources and optimizing retrieval strategies, RAG significantly improves performance. It enhances LLMs’ ability to deliver accurate and relevant information by focusing on the most pertinent data chunks.

It’s important to note, however, that simply deploying a RAG architecture is not enough. You must also optimize the way you chunk information, combine data sources, and retrieve and reason on that data. 

Advanced Content Retrieval Techniques

Effective document understanding hinges on sophisticated chunking strategies. Proper chunking enhances the performance of vector searches, enabling more precise retrieval of relevant information. Instabase AI Hub employs proprietary content representation to optimize chunking strategies with the RAG pattern, ensuring that items are appropriately chunked.

Two of the factors that differentiate the Instabase approach are the quality of the semantic information used and our holistic approach to system tuning.

Semantic information plays a crucial role in effective chunking. Headlines, tables, and visual objects each require specific approaches to ensure that their semantic value is preserved during the chunking process.

The chunking process must align with the data retrieval process for greater accuracy. Instead of optimizing each component individually, Instabase AI Hub emphasizes the importance of tuning the document understanding system as a holistic entity, ensuring seamless integration and superior performance.

Multi-Step Approach to Content Retrieval and Reasoning With AI Agents

Combining structured data with document retrieval processes is critical for specific use cases. For instance, comparing shipping rates from invoices with contractual rates stored in a database necessitates integrating structured data with document retrieval.

Once all of the content has been digitized, parsed, represented in a common format, and stored in the appropriate subsystems, it’s time to retrieve the data and reason on it.

At a basic level, LLMs are designed to work by answering simple questions. However, the type of complex document understanding use cases most businesses need simply cannot be solved with single-step LLM question answering. For many use cases, multi-step reasoning is needed. For example, some of the most important task types that require multi-step reasoning with tools are use cases where the model needs to take into account all occurrences within or across documents (e.g., find all contractual clauses of a certain type and compare them) and not just the top N chunks that are usually returned in a standard retrieval approach. 

Additionally, we know that LLMs are notoriously bad at math. Combining multi-step reasoning with tools such as calculators enables LLMs to produce accurate results to numerical questions and perform more advanced data analysis.For this reason, Instabase uses a multi-step approach that involves planning individual subtasks and using external tools to answer complex requests. After embedding the appropriate metadata, Instabase uses AI agent-based retrieval to decompose a task into its components, choosing appropriate tools for each component sub-task. 

Use Instabase to Clear Content Retrieval and Reasoning Hurdles

The advanced content retrieval and reasoning techniques employed by Instabase AI Hub address many of the common issues faced by LLMs in document understanding. By leveraging sophisticated chunking strategies, integrating structured data, and employing a holistic system tuning approach, Instabase AI Hub provides a robust solution for businesses in document-heavy industries looking for accurate, efficient document understanding solutions.

In the next article in this series, we’ll discuss how to prevent hallucinations and ensure data integrity when using LLMs for document understanding. For a deeper dive into how Instabase provides a solution superior to LLMs alone, download our whitepaper “LLMs Are Not All You Need: Full Stack Document Understanding with Instabase AI Hub.”