Towards Accurate and Efficient Document Analytics with Large Language Models (2405.04674v1)

Abstract: Unstructured data formats account for over 80% of the data currently stored, and extracting value from such formats remains a considerable challenge. In particular, current approaches for managing unstructured documents do not support ad-hoc analytical queries on document collections. Moreover, LLMs directly applied to the documents themselves, or on portions of documents through a process of Retrieval-Augmented Generation (RAG), fail to provide high accuracy query results, and in the LLM-only case, additionally incur high costs. Since many unstructured documents in a collection often follow similar templates that impart a common semantic structure, we introduce ZenDB, a document analytics system that leverages this semantic structure, coupled with LLMs, to answer ad-hoc SQL queries on document collections. ZenDB efficiently extracts semantic hierarchical structures from such templatized documents, and introduces a novel query engine that leverages these structures for accurate and cost-effective query execution. Users can impose a schema on their documents, and query it, all via SQL. Extensive experiments on three real-world document collections demonstrate ZenDB's benefits, achieving up to 30% cost savings compared to LLM-based baselines, while maintaining or improving accuracy, and surpassing RAG-based baselines by up to 61% in precision and 80% in recall, at a marginally higher cost.

• The paper introduces ZenDB, which extracts semantic hierarchical trees from documents to support ad-hoc SQL queries on unstructured data.
• It achieves significant cost savings up to 30× and improves precision and recall by up to 61% and 80%, respectively.
• The methodology integrates LLMs with structured query processing, setting the stage for scalable and efficient document analytics.

Overview of "Towards Accurate and Efficient Document Analytics with LLMs"

The paper presents ZenDB, a specialized document analytics system designed to tackle the challenges associated with querying unstructured document collections. It addresses a significant gap in existing approaches that employ LLMs and Retrieval-Augmented Generation (RAG) by leveraging the latent semantic structures often present in such documents.

Challenges in Document Querying

The paper begins by highlighting the prevalence of unstructured data formats and the difficulties encountered in extracting valuable information from them. The lack of support for ad-hoc analytical queries on unstructured documents poses a significant challenge. Existing methods using LLMs directly for document queries face limitations in accuracy, cost, and efficiency. The high cost of processing involved with LLMs, especially for tasks involving complex aggregations and filters on large document contexts, is a key barrier. RAG, though more cost-effective than LLMs, struggles with accurately selecting relevant text segments due to its limited understanding of document semantics.

The ZenDB Approach

ZenDB introduces a novel system that capitalizes on the semantic hierarchical structures inherent in many unstructured documents. It posits that documents created using similar templates impart a common, useful semantic structure that can be leveraged for querying. ZenDB efficiently extracts these semantic structures and incorporates them into SQL query processing, offering a system capable of imposing and querying schemas over document collections.

ZenDB's architecture is based on the notion of Semantic Hierarchical Trees (SHTs), which represent the semantic structure of a document as a tree. This representation allows the system to accurately map document sections to SQL schema components, facilitating efficient ad-hoc query execution. The paper provides evidence of ZenDB's significant advantages over traditional LLM and RAG approaches, with cost savings up to 30× compared to LLMs and improvements in precision and recall over RAG by up to 61% and 80%, respectively.

Methodologies and Results

The authors detail their methodology in transforming unstructured documents into SHTs, leveraging consistent visual patterns and LLMs to extract these semantic structures. They also outline their approach to schema definition, allowing users to specify and query document structures using standard SQL syntax extended for ZenDB’s purposes. The paper presents extensive experimentation across three real-world datasets, demonstrating ZenDB’s substantial cost reduction and accuracy maintenance or improvement over existing methods.

Implications and Future Directions

The practical implications of this research are profound, as ZenDB provides a scalable, efficient solution for businesses facing the challenge of obtaining structured insights from unstructured data. By effectively utilizing semantic document structures, ZenDB sets a precedent for future work in leveraging latent document semantics in data analytics.

Theoretically, this paper expands the limits of what is achievable with LLMs in data management by marrying these models with more traditional data processing structures. Future explorations might investigate how this approach can be adapted for documents lacking clear templates or extending beyond text to include other unstructured data forms like audio and video. Additionally, the development of more sophisticated methods to automatically recognize and leverage latent semantic patterns across a variety of document formats presents a promising avenue for research.

Overall, ZenDB contributes a significant advancement in the field of automated document analytics, offering an effective bridge between the capabilities of LLMs and the structured needs of SQL-based querying systems.