LLM Map Error Classification With Adaptive Data Prompts
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Solution Overview
Problem
Map service providers face challenges in integrating large language models (LLMs) for accurate and timely map feedback reporting due to outdated training data and the need to convert unstructured user feedback into structured data formats, which is costly and distracting for users.
Innovation Solution
A system leveraging LLMs to process natural language feedback, integrating mobile device sensors, and applying structured data formats to convert user inputs into actionable map updates, verifying and filtering feedback for accuracy, and generating reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional map feedback collection methods are used, then users can report map errors, but the process is costly and distracting for users due to manual data entry requirements
Solution Approach 1:
The system automatically processes map feedback using LLMs to extract, classify, and structure data from user inputs without requiring manual intervention. The LLM autonomously handles data transformation from unstructured user feedback to structured formats, reducing user burden while maintaining operational simplicity
Solution Approach 2:
The patent replaces manual mechanical data entry processes with LLM-based automated processing. Instead of users manually filling out structured forms, the LLM extracts and structures data from natural language inputs, substituting the mechanical interaction model with an intelligent processing system
2Measurement precision
If LLMs are specifically trained for map feedback classification, then classification accuracy improves, but training costs and time increase
Solution Approach 1:
The system performs preliminary classification using the LLM's general capabilities before detailed processing. By leveraging the LLM's pre-trained understanding of language and context, the system achieves effective classification without requiring additional specialized training, thus avoiding time loss while maintaining accuracy
Solution Approach 2:
The patent employs a universal LLM that can handle multiple map feedback classification tasks without requiring separate training for each error type. The LLM's multi-functional nature allows it to adapt to different classification needs through prompt engineering rather than retraining, reducing both time and resource requirements
3Manufacturing precision
If detailed data collection prompts are provided to LLM, then map error reporting accuracy improves, but user interaction time increases
Solution Approach 1:
The system implements progressive data collection where the LLM requests only the necessary data fields based on the specific map error type and available information. Instead of always presenting all possible prompts, the system dynamically adjusts the number and type of questions, using partial action to reduce user interaction time while maintaining sufficient accuracy through targeted data collection
Solution Approach 2:
The LLM performs preliminary analysis of user feedback to determine which additional data fields are actually needed for accurate classification. By pre-assessing the completeness and quality of initial inputs, the system can avoid requesting unnecessary information, thus reducing user interaction time while preserving report accuracy through intelligent prompt selection
Data Source
AI summary
An approach is provided for large language model (LLM) map feedback reporting. The approach involves, for example, processing an input specifying a map error/feedback using an LLM to classify a map error type. The approach also comprises determining a map error template based on the map error type that specifies structured data fields for the map error type. The approach further involves using the template to construct prompts for the LLM to generate questions to collect data items for populating the data fields, and providing the prompts to the LLM to generate the questions and collect the data items from the user. The approach further involves using template to generate additional prompts to generate a map error report of the data items in a structured data format, and providing the additional prompts to the LLM to generate the map error report for transmission to a map feedback system.