Tabular Generative AI Grounding With Traceable Intermediate Tables
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Solution Overview
Problem
Conventional Table QA models struggle with complex queries and tables, often producing hallucinated results due to their black-box nature, making it difficult to validate answers and leading to inaccurate downstream applications.
Innovation Solution
The Plan-of-Query (POQ) system decomposes queries into atomic steps, translates them into database manipulation commands, and generates intermediate tables, providing visibility into the reasoning process to prevent hallucinations and allow validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLM is integrated into Table QA models to enable complex reasoning, then the model's reasoning capability is improved, but the process becomes invisible and errors cannot be detected
Solution Approach 1:
The patent segments the LLM's reasoning process into discrete, traceable steps by introducing intermediate tables that represent each reasoning stage. Instead of a single black-box output, the reasoning is divided into multiple visible steps with intermediate results, allowing users to audit and validate each step while maintaining the LLM's complex reasoning capabilities.
Solution Approach 2:
The patent introduces intermediate tables as intermediary artifacts between the input query and the final answer. These intermediate tables serve as a mediator that makes the reasoning process transparent and verifiable, allowing users to see exactly how the LLM processes information without sacrificing the model's sophisticated reasoning abilities.
2Adaptability or versatility
If LLM is used to process queries through multiple intermediate steps, then the reasoning process becomes more complex and capable, but the opacity increases making validation difficult
Solution Approach 1:
The patent segments the complex reasoning process into discrete, labeled steps with corresponding intermediate tables. Each step is explicitly documented and traceable, transforming the opaque complex process into a series of transparent, auditable operations that maintain reasoning capability while reducing complexity opacity.
Solution Approach 2:
The patent uses visual highlighting and annotation techniques to make different parts of the reasoning process visible and distinguishable. By visually marking intermediate tables and reasoning steps, the system makes the complex process more transparent and easier to validate without sacrificing reasoning capability.
3Productivity
If conventional Table QA models operate in black box nature, then the model can process queries efficiently, but incorrect predictions and hallucinations cannot be easily uncovered
Solution Approach 1:
The patent segments the query processing into efficient LLM-based reasoning steps while introducing intermediate tables that document each step's input and output. This segmentation allows the system to maintain efficient processing through LLM capabilities while adding visibility to detect and prevent hallucinations through traceable intermediate results.
Solution Approach 2:
The patent implements feedback mechanisms by providing users with access to intermediate tables and reasoning steps, enabling them to validate and verify the process. This feedback loop allows users to detect incorrect predictions and hallucinations in real-time, improving reliability without significantly impacting processing efficiency.
Data Source
AI summary
Various methods and processes, apparatuses or systems, and media for generating a grounded answer to a query using a corresponding table are disclosed. The present disclosure provides decomposing a complex query into natural-language sub-queries or steps, which are then translated into database manipulation commands to sequentially transform input table into intermediate or simplified tables, until the input table is simplified for performing a final decision query. Each of the sub-queries may be associated with corresponding intermediate or simplified tables to ground the sub-queries.