Data Question Answering With Context-Aware Auxiliary Queries
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Solution Overview
Problem
Conventional data analysis techniques are tedious and time-consuming, and machine-learning models often require additional follow-up queries due to misinterpretation of context or intent, reducing efficiency and increasing resource expenditure.
Innovation Solution
A data question and answer module leveraging a large language model to derive answers to data questions and anticipate relevant follow-up questions, providing auxiliary information and explanations to enhance confidence and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine-learning models are used to automate data analysis, then efficiency is improved and mistakes are avoided, but the models may misinterpret context or intent requiring additional follow-up queries
Solution Approach 1:
The system performs preliminary actions by automatically generating and executing multiple follow-up queries before the user needs to interact with the data. The machine-learning model anticipates potential misinterpretations and proactively clarifies context by generating supplementary questions and queries, ensuring accurate data retrieval without requiring user intervention.
Solution Approach 2:
The system implements feedback mechanisms where the machine-learning model continuously monitors the quality of its context interpretation and adjusts its behavior accordingly. When potential misinterpretations are detected, the model automatically generates follow-up queries to refine its understanding, creating a feedback loop that improves reliability while maintaining high productivity.
2Ease of operation
If conventional data analysis techniques are used, then simplicity is maintained, but the process is tedious and time-consuming
Solution Approach 1:
The machine-learning model performs self-service by automatically executing data queries, generating follow-up questions, and refining its own understanding without requiring user involvement. The system independently manages the entire analysis process, maintaining operational simplicity while dramatically reducing the time required compared to conventional manual analysis techniques.
3Speed
If machine-learning models are used to answer data questions, then speed is improved, but additional follow-up queries are required due to misinterpretation
Solution Approach 1:
The system executes preliminary actions by pre-generating and pre-executing multiple follow-up queries in advance. The machine-learning model anticipates potential issues and resolves them before the user needs answers, eliminating the need for sequential follow-up interactions and maintaining high response speed throughout the entire query process.
Data Source
AI summary
Techniques for data question answering with auxiliary recommendations are described to enable efficient querying of data sets for answers to data questions based on a natural language input. In an example, a processing device is operable to receive a natural language input including a query, determine an additional query based on a context of the query, and query a machine-learning model using the query and the additional query. The processing device is further operable to receive, from the machine-learning model, a result including a quantitative answer to the query, an additional answer based on the additional query, and an explanation by the machine-learning model of how the machine-learning model generated the quantitative answer or the additional answer in response to the querying. The processing device is operable to present the result for display in a user interface.