Natural Language Query Disambiguation With Confidence-Gated Clarification
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Solution Overview
Problem
Existing systems struggle to accurately determine user intent from natural language queries, particularly when they contain typos, jargon, or other ambiguities, leading to irrelevant search results.
Innovation Solution
A generative pretrained transformer machine learning model processes natural language text and context to identify intent, using syntax from natural language completion paradigms, and iteratively generates clarifying questions to disambiguate user queries until a threshold confidence is reached, ultimately generating structured query steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a search engine performs search based on user query directly, then search speed is fast, but search accuracy deteriorates when user intent is not accurately reflected in the query
Solution Approach 1:
The query processing is divided into multiple stages: initial intent identification, confidence evaluation, conditional clarification question generation, and final query execution. This segmentation allows the system to handle simple queries quickly while providing enhanced processing only when necessary, thus improving accuracy without uniformly increasing complexity for all queries
Solution Approach 2:
The system performs preliminary intent identification and confidence evaluation before executing the search. By assessing confidence levels in advance, the system can determine whether clarification is needed before committing to a search query, thereby improving intent accuracy while maintaining efficient processing for high-confidence cases
2Measurement precision
If the system requests clarification questions for ambiguous queries, then user intent accuracy is improved, but interaction time increases
Solution Approach 1:
The system applies clarification only partially - specifically when the confidence score falls below a predetermined threshold. For high-confidence queries, the system proceeds directly to search without clarification, thus reducing unnecessary interaction time while still improving accuracy for ambiguous cases through targeted clarification questions
3Adaptability or versatility
If the system uses predefined query templates, then query structure is standardized, but adaptability to diverse user language deteriorates
Solution Approach 1:
The system introduces an intermediary intent identification layer that translates diverse natural language queries into standardized internal representations. This intermediary layer maintains adaptability to various user expressions while ensuring accurate mapping to the reporting service interface through structured intent classification, thus bridging the gap between linguistic diversity and systematic processing
Data Source
AI summary
A method, computer program product, and system are provided. A first natural language text is received via a user interface. A generative pretrained transformer machine learning model processes the first natural language text and a context to identify a first intent. The processing is based in part on a syntax determined from a set of natural language completion paradigms. The generative transformer machine learning model maps the first set of parameters to a first query. The mapping is associated with a first confidence. The generative transformer machine learning model processes the first set of parameters and the first query to generate a set of execution steps. The processing is performed when the first confidence satisfies a threshold. The set of execution steps is parsed into a query object that is forwarded to a reporting service.