Self-Directed Synthetic Dialogues and Revisions Technical Report

Understand

Synthetic data has become an important tool in the fine-tuning of language models to follow instructions and solve complex problems.

• Nevertheless, the majority of open data to date is often lacking multi-turn data and collected on closed models, limiting progress on advancing open fine-tuning methods.
• We introduce Self Directed Synthetic Dialogues (SDSD), an experimental dataset consisting of guided conversations of language models talking to themselves.
• The dataset consists of multi-turn conversations generated with DBRX, Llama 2 70B, and Mistral Large, all instructed to follow a conversation plan generated prior to the conversation.