Cross-Language Instruction Tuning for Zero-Shot Language Models
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Solution Overview
Problem
Instruction tuning for super-large language models is time and cost-consuming due to the need for constructing various pieces of task data and instructions, hindering efficient zero-shot performance improvement.
Innovation Solution
A method and system for performing instruction tuning using heterogeneous languages, involving the generation of cross-language instructions and datasets in multiple languages, allowing for instruction tuning across different languages with shared formats to reduce resource construction time and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If instruction tuning is performed using multiple languages with separate datasets and instructions, then zero-shot performance is improved, but resource construction time and cost increase
Solution Approach 1:
The patent creates a universal instruction template structure that can be applied across multiple languages. The same instruction template format (including task description, input, output, and answer sections) is used for both Korean and English datasets, allowing the system to handle multiple languages with a single unified framework rather than requiring separate processing systems for each language.
Solution Approach 2:
The patent changes the language parameter of the instruction templates while maintaining the same structural format. By translating instructions into different languages (Korean and English) while keeping the template structure identical, the system achieves multi-language support without increasing structural complexity, thus reducing construction time and cost.
2Reliability
If instruction tuning is performed using multiple languages with separate datasets and instructions, then zero-shot performance is improved, but resource construction cost increases
Solution Approach 1:
The patent creates a universal instruction template structure that can be applied across multiple languages. The same instruction template format (including task description, input, output, and answer sections) is used for both Korean and English datasets, allowing the system to handle multiple languages with a single unified framework rather than requiring separate processing systems for each language.
Solution Approach 2:
The patent copies the successful instruction template structure from one language to another. By replicating the same template format and structure across different languages, the system avoids the need to create entirely new instruction sets for each language, significantly reducing the time and cost required for resource construction while maintaining effectiveness.
3Productivity
If cross-language instruction tuning is performed with shared formats, then resource construction efficiency is improved, but instruction specificity may be reduced
Solution Approach 1:
The patent applies local quality by allowing language-specific content within the unified template structure. While the overall template format remains consistent across languages, the actual instruction text, task descriptions, and examples are customized for each specific language and task type, ensuring that each language receives appropriately localized instructions while maintaining structural consistency.
Data Source
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AI summary
A method and a system for performing instruction tuning of a language model by using heterogeneous languages according to an embodiment of the present invention can improve zero-shot performance and generalization performance of a language model by performing instruction tuning by using different heterogeneous languages.