Language Model Selection for Accurate Natural-Language Data Analysis
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Solution Overview
Problem
Current data analysis tools face challenges in accurately interpreting user questions due to the complexity of the field, leading to varying understandings and low accuracy in data analysis results.
Innovation Solution
A data analysis method that displays analysis controls corresponding to different language models, allowing users to select a suitable model, generate data analysis instructions based on user questions, and display results, while providing guidance through recommended questions and datasets to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conversational analysis is used in data analysis tools, then natural language processing capability is provided, but accuracy of data analysis decreases due to complex field caliber and similar expressions
Solution Approach 1:
The patent segments the single conversational analysis approach into multiple specialized language models (conversation language model, text generation language model, code language model). Each model is optimized for specific types of data analysis tasks, allowing the system to select the most appropriate model for each question rather than using a generic conversational model for all tasks.
Solution Approach 2:
The system dynamically changes the parameter of language model selection based on the characteristics of the input question. By analyzing the question type and matching it with the appropriate language model characteristics, the system adapts the analysis approach to suit different data analysis scenarios, thereby improving accuracy while maintaining natural language processing capability.
2Measurement precision
If multiple language models are provided for selection, then accuracy of data analysis is improved through appropriate model selection, but device complexity increases
Solution Approach 1:
The patent introduces a question analysis module as an intermediary between the user's natural language input and the multiple language models. This module analyzes the input question, determines the appropriate language model to use, and routes the question to the selected model. This intermediary layer manages the complexity by providing a systematic approach to model selection rather than requiring direct user knowledge of multiple models.
Solution Approach 2:
The system design allows a single data analysis interface to universally handle multiple types of questions by supporting multiple language models. The interface maintains a unified user experience while internally managing multiple specialized models, making the system multi-functional without increasing the apparent complexity for the user.
3Adaptability or versatility
If different data analysis tools have different understandings of the same question, then each tool has its own interpretation capability, but consistency and reliability of analysis results decrease
Solution Approach 1:
The patent assigns different specialized characteristics to different language models based on their local quality or strength. Each model is optimized for specific types of data analysis tasks (e.g., conversation analysis, text generation, code analysis). By matching the question type with the model's specialized strength, the system achieves both adaptability to different question types and consistency in results through appropriate model-question alignment.
Data Source
AI summary
The present disclosure provides a data analysis method and apparatus, an electronic device, a storage medium, and a program product. The method includes: displaying a data analysis page, where an analysis control is displayed on the data analysis page, and the analysis control corresponds to at least one language model; determining a target language model in response to a selection instruction for the analysis control; generating a first data analysis instruction in response to a first question determined on the data analysis page, where the first data analysis instruction is used to determine a first analysis result of the target language model for the first question; and displaying the first analysis result on the data analysis page.