Data Question Answering with Context-Aware Auxiliary Recommendations
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Solution Overview
Problem
Conventional data analysis techniques are tedious and time-consuming, and machine-learning models often require additional user inputs to provide accurate answers due to misinterpretation of context or intent, leading to inefficiency.
Innovation Solution
A data question and answer module leveraging a large language model to derive answers to data questions and anticipate 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 answer data questions, then efficiency is improved, but the model may misinterpret context or intent requiring additional user inputs
Solution Approach 1:
The system performs preliminary actions by automatically identifying and answering anticipated follow-up questions before the user asks them. The machine-learning model predicts what information the user will need based on the initial query and proactively retrieves and presents that information, eliminating the need for multiple sequential user inputs and reducing overall interaction time.
Solution Approach 2:
The system implements feedback mechanisms where the machine-learning model continuously monitors the conversation context and adjusts its responses accordingly. By analyzing the initial query and predicted follow-up questions, the model refines its understanding of context and intent, providing more accurate and relevant information with each interaction cycle.
2Measurement precision
If additional user inputs are required to clarify context, then answer accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs the action of anticipating and preparing answers to follow-up questions before the user actually asks them. By predicting the likely follow-up questions and pre-retrieving the necessary information, the system eliminates the time the user would spend formulating and inputting clarification questions, while still maintaining high answer accuracy through informed predictions.
Solution Approach 2:
The system serves itself by automatically determining what additional information is needed and retrieving it without requiring user intervention. The machine-learning model autonomously identifies gaps in the initial query, predicts necessary follow-up questions, and proactively gathers the required data, making the system self-sufficient in clarifying context without user inputs.
3Ease of manufacture
If conventional data analysis techniques are used, then simplicity is maintained, but productivity is reduced due to tediousness
Solution Approach 1:
The system replaces manual mechanical data analysis processes with an automated machine-learning-based system. Instead of requiring users to manually query, filter, and analyze data through multiple steps, the machine-learning model automatically interprets queries, identifies relevant data, performs analysis, and generates answers, dramatically increasing productivity while maintaining ease of use through natural language interaction.
Solution Approach 2:
The system enables self-service data analysis where users can ask questions in natural language and receive automated answers without manual intervention. The machine-learning model autonomously handles the entire analysis process including data retrieval, processing, and interpretation, eliminating the tedious manual steps while maintaining simplicity for the user.
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.