The ability of large language models (LLMs) to follow instructions is crucial
to real-world applications. Despite recent advances, several studies have
highlighted that LLMs struggle when faced with challenging instructions,
especially those that include complex constraints, hindering their
effectiveness in various tasks. To address this challenge, we introduce
Conifer, a novel instruction tuning dataset, designed to enhance LLMs to follow
multi-level instructions with complex constraints. Utilizing GPT-4, we curate
the dataset by a series of LLM-driven refinement processes to ensure high
quality. We also propose a progressive learning scheme that emphasizes an
easy-to-hard progression, and learning from process feedback. Models trained
with Conifer exhibit remarkable improvements in instruction-following
abilities, especially for instructions with complex constraints. On several
instruction-following benchmarks, our 7B model outperforms the state-of-the-art
open-source 7B models, even exceeds the performance of models 10 times larger
on certain metrics. All the code and Conifer dataset are available at
this https URL

通过引入名为 Conifer 的新型指令调整数据集，以及采用渐进学习方案和学习过程反馈，我们提高了大型语言模型（LLMs）在遵循具有复杂约束的多级指令方面的能力，并在几个指令遵循基准测试中，实现了与现有 7B 模型相比的显著改进，甚至在某些度量标准上超过了 10 倍大的模型的性能。