OpenAssistant Conversations - Democratizing Large Language Model Alignment
PROVISIO GmbH · ETH Zurich · iKala, National Chiao Tung University · University of California, Los Angeles · Freie Universität Berlin · University of Technical Education Ho Chi Minh City · Kainos · Eötvös Loránd University · Exploding Gradients · Thousand Oaks High School · National University of Science and Technology «MISIS» · Georgia Institute of Technology · LAION e.V.
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摘要
Aligning large language models (LLMs) with human preferences has proven to drastically improve usability and has driven rapid adoption as demonstrated by ChatGPT.Alignment techniques such as supervised fine-tuning (\textit{SFT}) and reinforcement learning from human feedback (\textit{RLHF}) greatly reduce the required skill 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 \textit{RLHF} rely on high-quality human feedback data, which is expensive to create and often remains proprietary.In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations, a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 complete and fully annotated conversation trees.The corpus is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers.Models trained on OpenAssistant Conversations show consistent improvements on standard benchmarks over respective base models.We release our code\footnote{\git} and data\footnote{\data} under a fully permissive licence.