  Instruction tuning as an effective technique aligns the outputs of large
language models (LLMs) with human preference. But how to generate the seasonal
multi-turn dialogues from raw documents for instruction tuning still requires
further exploration. In this paper, we present a novel framework named R2S that
leverages the CoD-Chain of Dialogue logic to guide large language models (LLMs)
in generating knowledge-intensive multi-turn dialogues for instruction tuning.
By integrating raw documents from both open-source datasets and domain-specific
web-crawled documents into a benchmark K-BENCH, we cover diverse areas such as
Wikipedia (English), Science (Chinese), and Artifacts (Chinese). Our approach
first decides the logic flow of the current dialogue and then prompts LLMs to
produce key phrases for sourcing relevant response content. This methodology
enables the creation of the G I NSTRUCT instruction dataset, retaining raw
document knowledge within dialoguestyle interactions. Utilizing this dataset,
we fine-tune GLLM, a model designed to transform raw documents into structured
multi-turn dialogues, thereby injecting comprehensive domain knowledge into the
SFT model for enhanced instruction tuning. This work signifies a stride towards
refining the adaptability and effectiveness of LLMs in processing and
generating more accurate, contextually nuanced responses across various fields.
