Instruction Similarity Filtering for Zero-Shot AI Training Data
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Solution Overview
Problem
Existing methods for improving zero-shot learning performance in AI models through instruction tuning require excessive computing resources and may lead to negative transfer due to irrelevant training tasks, affecting model performance.
Innovation Solution
A system and method for constructing a dataset that evaluates similarity between training and target task instructions using cosine similarity or model transfer, selecting instructions with high similarity, and optionally using a pre-tuned task selector model to enhance accuracy, while removing placeholders to prevent negative effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all instructions and instances from tasks are used for training to improve zero-shot learning performance, then model performance is improved, but computing resources are excessively consumed
Solution Approach 1:
The patent extracts only the instruction portion from task data, selectively taking out the relevant component (instructions) while excluding less useful components (instances). This extraction enables the model to learn task structures and perform zero-shot learning without processing all task data, thereby reducing computing resource consumption while maintaining performance improvement.
Solution Approach 2:
The patent segments task data into distinct components (instructions and instances) and processes only the instruction segment for training. This segmentation allows the model to focus on learning task definitions and structures from instructions alone, avoiding the computational burden of processing all task instances while still achieving zero-shot learning capabilities.
2Quantity of substance
If training tasks with no relevance to target task are included to increase training data volume, then training data quantity is increased, but negative transfer occurs degrading model performance
Solution Approach 1:
The patent applies local quality by selecting and training only on instructions that are locally relevant to the target task. Instead of uniformly processing all task instructions, the system identifies and focuses on instructions with high relevance to the target task, thereby increasing training data quantity for relevant tasks while avoiding the inclusion of irrelevant tasks that would cause negative transfer.
Data Source
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AI summary
A system, method, and program for constructing a data set that improves zero-shot learning performance of an AI model through instruction tuning are disclosed. The method includes extracting instructions from each of a training task used for training an AI model, and a target task that is a task to be trained through the training task, evaluating similarity by comparing the extracted instruction of the training task with the extracted instruction of the target task, selecting, from among the extracted instructions of the training task, instructions having a similarities equal to or greater than a predetermined value, and outputting the selected instructions of the training task as a data set.