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DS$^2$-Instruct: Domain-Specific Data Synthesis for Large Language Models Instruction Tuning

arXiv:2603.12932v1 Announce Type: new Abstract: Adapting Large Language Models (LLMs) to specialized domains requires high-quality instruction tuning datasets, which are expensive to create through human annotation. Existing data synthesis methods focus on general-purpose tasks and fail to capture domain-specific terminology and reasoning patterns. To address this, we introduce DS$^2$-Instruct, a zero-shot framework that generates domain-specific instruction datasets without human supervision. Our approach first generates task-informed keywords to ensure comprehensive domain coverage. It then creates diverse instructions by pairing these keywords with different cognitive levels from Bloom's Taxonomy. Finally, it uses self-consistency validation to ensure data quality. We apply this framework to generate datasets across seven challenging domains, such as mathematics, finance, and logical reasoning. Comprehensive evaluation demonstrates that models fine-tuned on our generated data achie

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Ruiyao Xu, Noelle I. Samia, Han Liu · · 1 min read · 9 views
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arXiv:2603.12932v1 Announce Type: new Abstract: Adapting Large Language Models (LLMs) to specialized domains requires high-quality instruction tuning datasets, which are expensive to create through human annotation. Existing data synthesis methods focus on general-purpose tasks and fail to capture domain-specific terminology and reasoning patterns. To address this, we introduce DS$^2$-Instruct, a zero-shot framework that generates domain-specific instruction datasets without human supervision. Our approach first generates task-informed keywords to ensure comprehensive domain coverage. It then creates diverse instructions by pairing these keywords with different cognitive levels from Bloom's Taxonomy. Finally, it uses self-consistency validation to ensure data quality. We apply this framework to generate datasets across seven challenging domains, such as mathematics, finance, and logical reasoning. Comprehensive evaluation demonstrates that models fine-tuned on our generated data achieve substantial improvements over existing data generation methods.

Executive Summary

DS$^2$-Instruct introduces a novel zero-shot framework for generating domain-specific instruction datasets without human supervision. This approach leverages task-informed keywords, Bloom's Taxonomy, and self-consistency validation to create comprehensive and diverse datasets. The framework is applied across seven challenging domains, demonstrating substantial improvements in model performance over existing data generation methods. While DS$^2$-Instruct offers a promising solution for adapting Large Language Models to specialized domains, its limitations and potential applications warrant further exploration.

Key Points

  • ▸ Proposes a zero-shot framework for generating domain-specific instruction datasets
  • ▸ Utilizes task-informed keywords and Bloom's Taxonomy to ensure comprehensive domain coverage
  • ▸ Employs self-consistency validation for data quality control
  • ▸ Achieves substantial improvements in model performance over existing data generation methods

Merits

Strength in Addressing Domain-Specific Challenges

DS$^2$-Instruct effectively addresses the limitations of existing data synthesis methods by generating domain-specific instruction datasets, which are crucial for adapting Large Language Models to specialized domains.

Improved Model Performance

The proposed framework demonstrates substantial improvements in model performance over existing data generation methods, showcasing its potential in real-world applications.

Efficient Data Generation

DS$^2$-Instruct achieves zero-shot data generation, eliminating the need for human supervision and reducing the costs associated with creating high-quality instruction tuning datasets.

Demerits

Limitation in Handling Complex Domains

The framework may struggle to capture intricate domain-specific terminology and reasoning patterns, particularly in complex domains, which could impact its effectiveness.

Dependence on Task-Informed Keywords

The quality of generated datasets relies heavily on the accuracy of task-informed keywords, which may not always be readily available or reliable.

Expert Commentary

DS$^2$-Instruct represents a significant advancement in the field of natural language processing, addressing the long-standing challenge of adapting Large Language Models to specialized domains. While the proposed framework demonstrates impressive results, its limitations and potential applications warrant further exploration. As the field continues to evolve, it is crucial to develop and refine methods for generating high-quality, domain-specific instruction datasets that can effectively support the training and fine-tuning of Large Language Models.

Recommendations

  • ✓ Future research should focus on refining the task-informed keyword generation process to improve the accuracy and reliability of generated datasets.
  • ✓ The development of DS$^2$-Instruct highlights the need for interdisciplinary collaboration between natural language processing researchers, domain experts, and regulatory bodies to ensure the effective application of large language models in specialized domains.

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