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Aligning large language models (LLMs) with human preferences has proven todrastically improve usability and has driven rapid adoption as demonstrated byChatGPT. Alignment techniques such as supervised fine-tuning (SFT) andreinforcement learning from human feedback (RLHF) greatly reduce the requiredskill and domain knowledge to effectively harness the capabilities of LLMs,increasing their accessibility and utility across various domains. However,state-of-the-art alignment techniques like RLHF rely on high-quality humanfeedback data, which is expensive to create and often remains proprietary. Inan effort to democratize research on large-scale alignment, we releaseOpenAssistant Conversations, a human-generated, human-annotated assistant-styleconversation corpus consisting of 161,443 messages in 35 different languages,annotated with 461,292 quality ratings, resulting in over 10,000 complete andfully annotated conversation trees. The corpus is a product of a worldwidecrowd-sourcing effort involving over 13,500 volunteers. Models trained onOpenAssistant Conversations show consistent improvements on standard benchmarksover respective base models. We release our code and data under a fullypermissive licence.

OpenAssistant Conversations -- Democratizing Large Language Model Alignment