Multi-Channel Prompt Selection for Reliable LLM Text Output
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Solution Overview
Problem
Traditional large language models (LLMs) suffer from inaccurate hallucinations and lack reliable quality assessment techniques for generative text, making prompt creation time-consuming and case-specific, which limits their practicality and reliability across diverse use cases.
Innovation Solution
A multi-stage generative framework with multi-channel assessment techniques to create, score, and select optimal prompts for LLMs, including a multi-stage text matching pipeline and prompt selection pipeline, enabling automatic prompt creation and quality assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional prompting techniques are used to guide text generation, then the reliability of generative text output may be improved, but the time and cost required to create quality prompts increases significantly
Solution Approach 1:
The system enables prompts to self-evaluate their own quality by generating multiple candidate prompts, assessing them through multiple channels (text matching, quality metrics, generative performance), and automatically selecting the optimal prompt without human intervention, thus resolving the contradiction between reliability and time investment
Solution Approach 2:
The framework implements a feedback mechanism where generated text is evaluated against multiple quality metrics and channels, and this feedback is used to iteratively improve prompt selection and creation, allowing the system to learn from past performance and continuously enhance reliability while maintaining efficiency
2Measurement precision
If quality prompts are manually created for specific cases, then the accuracy of generative text output improves, but the adaptability to diverse use cases decreases
Solution Approach 1:
The system creates a universal prompt assessment framework that can evaluate and select prompts across multiple diverse use cases and domains. The multi-channel assessment approach (text matching, quality metrics, generative evaluation) is domain-agnostic and can be applied universally to different types of generative tasks, thereby maintaining both accuracy and adaptability
Solution Approach 2:
The framework dynamically adjusts assessment parameters and weights based on the specific use case and domain requirements. By changing the relevant quality metrics and evaluation criteria according to the task at hand, the system maintains high accuracy across diverse applications without requiring manual prompt creation for each case
3Reliability
If multiple quality assessment channels are implemented, then the holistic quality assessment of prompts improves, but the device complexity increases
Solution Approach 1:
The assessment framework is segmented into distinct independent channels (text matching pipeline, quality metrics pipeline, generative text evaluation pipeline), each handling a specific aspect of prompt quality. This modular segmentation reduces complexity by allowing each channel to be developed and maintained independently while collectively providing holistic assessment
Data Source
AI summary
Various embodiments of the present disclosure provide prompt engineering and text quality assessment techniques for improving generative text outputs. The techniques include identifying a training cluster for an input document, generating a candidate prompt for a generative machine learning model based on the training cluster and a prompt template, providing the candidate prompt to the generative machine learning model to receive at least a portion of a candidate document, generating a plurality of quality metrics for the candidate prompt based on the candidate document, and selecting the candidate prompt from a plurality of candidate prompts based on the plurality of quality metrics.