Large-Model Query Prompting With Automatic Example Pair Retrieval
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Solution Overview
Problem
Data query methods based on natural language require high-quality examples for large models, but manually selecting examples is time-consuming and non-scalable, leading to inefficiencies and inaccuracies in generating query statements.
Innovation Solution
An information presentation method that matches query questions against a set of target example pairs to obtain reference example pairs, using these to guide a large model in generating query statements, thereby reducing manual workload and improving accuracy and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manually selecting examples for large model training, then example quality can be controlled, but time consumption increases and scalability deteriorates
Solution Approach 1:
The system automatically selects and retrieves example pairs based on query question characteristics without requiring manual intervention. The retrieval module autonomously matches query questions against stored example pairs to generate appropriate prompts for the large model, eliminating the time-consuming manual selection process while maintaining example quality through automated filtering and matching mechanisms.
2Reliability
If manually selecting examples for large model training, then example quality can be controlled, but scalability deteriorates
Solution Approach 1:
The automated retrieval module enables the system to handle diverse query questions across different domains without requiring manual example selection for each case. The system self-adapts by automatically matching query characteristics against stored examples, allowing scalable deployment across multiple applications and query types while maintaining consistent example quality through automated filtering and matching mechanisms.
3Adaptability or versatility
If using automated example selection, then scalability improves, but accuracy deteriorates
Solution Approach 1:
The system replaces manual mechanical selection processes with automated information processing mechanisms. The retrieval module uses automated matching algorithms to select example pairs based on query question characteristics, and the prompt construction module automatically formats these examples into structured prompts. This substitution maintains accuracy by using intelligent automated filtering and matching rather than simple mechanical selection, enabling both scalability and precision.
Data Source
AI summary
An information presentation method based on a large model, a device, and a medium, which relate to the field of data processing technologies, and in particular to the field of artificial intelligence technologies such as large models, natural language processing, and deep learning. The method includes: matching, in response to receiving a query question, the query question against a set of target example pairs corresponding to a query type of the query question to obtain at least one reference example pair, where the large model is configured to generate a query statement for the query question using the reference example pair; invoking the large model according to a prompt information to generate a target query statement, where the prompt information is obtained based on the query question and the at least one reference example pair; and presenting a query result obtained by executing the target query statement.