(InThe)WildChat: 570K ChatGPT Interaction Logs In The Wild

Wenting Zhao,Xiang Ren, Jack Hessel,Claire Cardie,Yejin Choi, Yuntian Deng

ICLR 2024(2024)

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Chatbots such as GPT-4 and ChatGPT are now serving millions of users. Despite their widespread use, there remains a lack of public datasets showcasing how these tools are used by a population of users in practice. To bridge this gap, we offered free access to ChatGPT for online users in exchange for their affirmative, consensual, opt-in for anonymous collection of their chat transcripts. From this, we compiled (InThe)WildChat, a corpus of 570K user-ChatGPT conversations, which consists of over 1.5 million interaction turns. We compare WildChat with other popular user-chatbot interaction datasets, and find that our dataset offers the most diverse user prompts, contains the largest number of languages, and presents the richest variety of potentially toxic use-cases for researchers to study. In particular, in WildChat we find that a majority of the potentially unsafe use is produced by users attempting to “jailbreak” the model using prompts posted on online platforms; these are successful more than 70% of the time for ChatGPT. Finally, because it captures a broad range of use cases, we demonstrate the dataset’s potential utility in fine-tuning state-of-the-art instruction following models. WildLlama, a chatbot fine-tuned on WildChat, outperforms the latest Vicuna model of the same size on MT-Bench, which shows that WildChat has a high utility in addition to being a source for toxicity study. We will release WildChat and WildLlama with a license that emphasizes on accountability, collaboration, and transparency. The clean portion of WildChat will be publicly available, and the portion that contains potentially unsafe content will be made available upon request with a justification for AI safety research.
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Key words
new corpus,user study,dialogues,conversations,chatGPT,instruction fine-tuning,toxicity,safe guarding,AI safety
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