From Complex to Simple: Enhancing Multi-Constraint Complex Instruction Following Ability of Large Language Models
Abstract: It is imperative for Large language models (LLMs) to follow instructions with elaborate requirements (i.e. Complex Instructions Following}). Yet, it remains under-explored how to enhance the ability of LLMs to follow complex instructions with multiple constraints. To bridge the gap, we initially study what training data is effective in enhancing complex constraints following abilities. We found that training LLMs with instructions containing multiple constraints enhances their understanding of complex instructions, especially those with lower complexity levels. Additionally, we further propose methods addressing how to obtain and utilize the effective training data. Finally, we conduct extensive experiments to prove the effectiveness of our methods in terms of overall performance and training efficiency. We also demonstrate that our methods improve models' ability to follow instructions generally and generalize effectively across out-of-domain, in domain, and adversarial settings, while maintaining general capabilities.
Paper Type: Long
Research Area: Language Modeling
Research Area Keywords: fine-tuning, continual learning
Contribution Types: Model analysis & interpretability, NLP engineering experiment
Languages Studied: English
Submission Number: 4662
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