Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through
Active Exploration
- URL: http://arxiv.org/abs/2310.09168v3
- Date: Tue, 24 Oct 2023 06:55:17 GMT
- Title: Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through
Active Exploration
- Authors: Fanqi Wan, Xinting Huang, Tao Yang, Xiaojun Quan, Wei Bi, Shuming Shi
- Abstract summary: Explore-Instruct is a novel approach to enhance the data coverage to be used in domain-specific instruction-tuning.
Our data-centric analysis validates the effectiveness of this proposed approach in improving domain-specific instruction coverage.
Our findings offer a promising opportunity to improve instruction coverage, especially in domain-specific contexts.
- Score: 64.58185031596169
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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
\url{https://github.com/fanqiwan/Explore-Instruct}.
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