LessWrong Amplify Instruct: Multi-Turn AI Safety Conversations

LDJnr's LessWrong Amplify Instruct dataset contains over 500 multi-turn synthetic conversations. The dataset leverages the Amplify-Instruct method to extend thousands of scraped LessWrong posts into advanced, in-depth dialogues. It was last updated on June 3, 2024.

Use Cases

• Fine-tuning conversational AI models based on multi-turn dialogue examples.
• Training models for advanced reasoning and argumentation based on in-depth conversation structure.
• Benchmarking model performance on long-context, multi-turn interactions based on the described average token length.
• Studying synthetic data generation techniques for instruction tuning based on the Amplify-Instruct method.

Strengths

• Contains over 500 multi-turn conversation examples.
• Average context length per conversation is over 2,000 tokens.
• Uses a described 'Amplify-Instruct' pipeline for synthetic generation.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count beyond the initial 500 examples is unknown, which may limit suitability assessment.
• Freshness should be verified as the last update was 2024-06-03.

Provenance

Source

LDJnr on Hugging Face.

Collection Method

Synthetically created using a pipeline that extends scraped LessWrong posts via the Amplify-Instruct method.

Freshness

Last updated 2024-06-03 01:47:31.