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    <abstract>Instruction-tuning can be substantially optimized through enhanced diversity, resulting in models capable of handling a broader spectrum of tasks. However, existing data employed for such tuning often exhibit an inadequate coverage of individual domains, limiting the scope for nuanced comprehension and interactions within these areas. To address this deficiency, we propose Explore-Instruct, a novel approach to enhance the data coverage to be used in domain-specific instruction-tuning through active exploration via Large Language Models (LLMs). Built upon representative domain use cases, Explore-Instruct explores a multitude of variations or possibilities by implementing a search algorithm to obtain diversified and domain-focused instruction-tuning data. Our data-centric analysis validates the effectiveness of this proposed approach in improving domain-specific instruction coverage. Moreover, our model’s performance demonstrates considerable advancements over multiple baselines, including those utilizing domain-specific data enhancement. Our findings offer a promising opportunity to improve instruction coverage, especially in domain-specific contexts, thereby advancing the development of adaptable language models. Our code, model weights, and data are public at https://github.com/fanqiwan/Explore-Instruct.</abstract>
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%0 Conference Proceedings
%T Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration
%A Wan, Fanqi
%A Huang, Xinting
%A Yang, Tao
%A Quan, Xiaojun
%A Bi, Wei
%A Shi, Shuming
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wan-etal-2023-explore
%X Instruction-tuning can be substantially optimized through enhanced diversity, resulting in models capable of handling a broader spectrum of tasks. However, existing data employed for such tuning often exhibit an inadequate coverage of individual domains, limiting the scope for nuanced comprehension and interactions within these areas. To address this deficiency, we propose Explore-Instruct, a novel approach to enhance the data coverage to be used in domain-specific instruction-tuning through active exploration via Large Language Models (LLMs). Built upon representative domain use cases, Explore-Instruct explores a multitude of variations or possibilities by implementing a search algorithm to obtain diversified and domain-focused instruction-tuning data. Our data-centric analysis validates the effectiveness of this proposed approach in improving domain-specific instruction coverage. Moreover, our model’s performance demonstrates considerable advancements over multiple baselines, including those utilizing domain-specific data enhancement. Our findings offer a promising opportunity to improve instruction coverage, especially in domain-specific contexts, thereby advancing the development of adaptable language models. Our code, model weights, and data are public at https://github.com/fanqiwan/Explore-Instruct.
%R 10.18653/v1/2023.emnlp-main.587
%U https://aclanthology.org/2023.emnlp-main.587
%U https://doi.org/10.18653/v1/2023.emnlp-main.587
%P 9435-9454

Markdown (Informal)

[Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration](https://aclanthology.org/2023.emnlp-main.587) (Wan et al., EMNLP 2023)

• Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration (Wan et al., EMNLP 2023)

ACL

• Fanqi Wan, Xinting Huang, Tao Yang, Xiaojun Quan, Wei Bi, and Shuming Shi. 2023. Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 9435–9454, Singapore. Association for Computational Linguistics.