Coherent Hypergraph Retrieval for Long-Document Question Answering
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Solution Overview
Problem
Existing document question answering (QA) techniques, particularly large language models (LLMs), are prone to incoherent answers and hallucinations, especially when dealing with long documents, and struggle to accurately extract coherent information from multiple entities.
Innovation Solution
A system that generates hypergraphs with nodes representing entities and hyperedges representing relationships between them, associated with probabilistic weights, to select coherent hyperedges for natural language questions, using a foundation model and generative LLM to generate accurate answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large language models are used for document question answering, then the system can process long documents, but the answers become incoherent and contain hallucinations
Solution Approach 1:
The patent segments the document processing into two distinct stages: first, a foundation model extracts entities and relationships and constructs a hypergraph representation; second, a generative LLM queries this hypergraph to produce answers. This segmentation allows each model to specialize - the foundation model handles comprehensive document understanding while the generative LLM produces coherent, verified answers, thereby resolving the contradiction between processing long documents and maintaining answer reliability.
Solution Approach 2:
The patent introduces a hypergraph as an intermediary structure between the document and the final answer. The hypergraph serves as a mediator that captures coherent relationships between entities in the document, allowing the generative LLM to query verified relationships rather than generating answers directly from the document text. This intermediary structure prevents hallucinations while enabling processing of long documents.
2Device complexity
If traditional QA techniques are used, then the system is simpler, but it cannot accurately extract coherent information from multiple entities
Solution Approach 1:
The patent transitions from traditional binary edge representations to hyperedges that can connect multiple entities simultaneously, adding a dimensional aspect to relationship representation. This hypergraph structure enables the system to capture multi-entity relationships and their interactions, significantly improving the accuracy of information extraction from multiple entities while maintaining reasonable system complexity through the use of foundation models for initial processing.
Data Source
AI summary
Systems or techniques that facilitate coherent hyperedges for document QA are provided. In various embodiments, a system can generate, via a foundation model, a hypergraph comprising nodes and hyperedges, wherein the nodes represent entities in an information source, wherein the hyperedges represent relationships between two or more of the nodes, and wherein the hyperedges are associated with respective probabilistic weights. In various cases, the system can further select, from the hyperedges, a set of coherent hyperedges for a natural language question that represents coherent information from the information source.