User Request Prompt Generation for Few-Shot Response Accuracy
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Solution Overview
Problem
Conventional machine learning algorithms face challenges in few-shot scenarios due to the limited availability of training data, making it difficult to learn patterns and extract features effectively.
Innovation Solution
The method leverages in-context learning capabilities of language models by utilizing a reference prompt and a set of reference samples to generate a prompt for processing user requests without the need for model training, using a verified reference prompt library and sample library to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning algorithms are used with limited training data, then the model training process can be completed, but the model cannot effectively learn patterns and extract features
Solution Approach 1:
The patent applies preliminary action by pre-collecting and organizing reference samples and prompts before the actual model training. The system prepares a database of reference data including sample inputs, outputs, and prompts in advance, which can be directly utilized when training data is limited, eliminating the need to start from scratch with minimal data.
Solution Approach 2:
The patent introduces prompts as an intermediary element between the input data and the model processing. These prompts guide the model's attention and help it effectively process limited training data by providing contextual information and task-specific instructions, bridging the gap between scarce data and effective learning.
2Reliability
If a large amount of labeled training data is collected, then the model can learn patterns effectively, but the data collection and processing complexity increases
Solution Approach 1:
The patent extracts and utilizes existing reference samples and prompts from a pre-built database rather than collecting and processing large amounts of new training data. This extraction approach allows the model to learn effective patterns by leveraging previously curated reference data, avoiding the complexity of large-scale data collection and processing systems.
3Reliability
If extensive model training is performed, then the model performance improves, but the training time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing reference data, prompts, and sample configurations before training. This preparation work is done in advance, allowing the actual model training to proceed more efficiently with less computational effort and time, as the foundation for learning is already established.
Solution Approach 2:
The patent utilizes reference samples and prompts that can be copied and reused across different training scenarios. Instead of creating new training materials from scratch for each model training session, the system copies and adapts proven reference data, significantly reducing the time and resources required for repeated training processes.
Data Source
AI summary
A method, an apparatus, a device, and a medium for processing a user request are provided. In the method, in response to receiving the user request, a reference prompt matching the user request is determined; a set of reference samples matching the user request is determined, wherein a reference sample in the set of reference samples comprises a reference user request and a reference response for the reference user request, and a task type specified by the user request is the same as a reference task type specified by the reference user request; and a prompt for executing the user request is generated based on the user request, the reference prompt, and the set of reference samples.