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🌈 Abstract

The article discusses the creation of a new dataset called Self-Directed Synthetic Dialogues (SDSD), which consists of multi-turn conversations generated by language models talking to themselves. The goal is to create a dataset that can be used to fine-tune language models to engage in long-form, open-ended conversations, going beyond the single-turn instruction datasets that are commonly used. The dataset also includes preference data generated through a process inspired by Constitutional AI, where the language model's responses are critiqued for violations of specified principles, and then rewritten.

🙋 Q&A

[01] Self-Directed Synthetic Dialogues (SDSD)

1. What is the purpose of the SDSD dataset? The purpose of the SDSD dataset is to create a multi-turn, open-ended conversational dataset that can be used to fine-tune language models, going beyond the single-turn instruction datasets that are commonly used.

2. How is the SDSD dataset generated? The SDSD dataset is generated through a process where language models are instructed to have conversations with themselves, following a pre-defined plan that includes a topic, principles to be violated, and a goal for the conversation. If the language model violates one of the specified principles, the final turn of the conversation is rewritten through a critique and revision process.

3. What are the key components of the SDSD dataset generation process? The key components are:

• Topics and subtopics: A list of topics and subtopics to seed the conversations
• Principles: A set of principles, compiled from sources like Anthropic's research, that the language model should try to violate
• Goals: A set of tasks or objectives that the conversation should cover
• Dialogue generation: The language model follows the plan to generate a multi-turn conversation
• Revision generation: If a principle is violated, the final turn is rewritten through a critique and revision process

4. How does the SDSD dataset compare to other instruction and preference datasets? Compared to other instruction datasets, SDSD has longer conversations with more turns, and the language is more concise. Compared to preference datasets, SDSD has more turns and a larger number of revision examples.

[02] Dataset Analysis

1. What are the key statistics of the SDSD dataset? The SDSD dataset contains over 100,000 examples, with an average of 3-6 turns per conversation, and an average prompt length of 20-30 tokens and response length of 40-60 tokens.

2. How are the principles distributed in the SDSD dataset? The distribution of principle violations is not uniform, with some principles being violated much more frequently than others. The top violated principles are related to not providing misinformation, being thoughtless or disrespectful, and lacking ethical and moral awareness.

3. What are the limitations and lessons learned from generating the SDSD dataset? Some key limitations and lessons include:

• Automatic filtering and verification is required to handle issues like missing end-of-message tokens or rare errors in instruction following
• Debugging and analysis of each stage of the pipeline is important, as metrics like length and diversity may not capture all issues
• Generating high-quality critiques and revisions is challenging for language models, and may require the use of stronger models like GPT-4
• Balancing the distribution of topics and principles in the procedurally generated dataset can be difficult, and may require normalization based on previous data