15 research outputs found

    An IDR Framework of Opportunities and Barriers between HCI and NLP

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    Aiki - Turning Online Procrastination into Microlearning

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    Evaluating Academic Reading Support Tools: Developing the aRSX-Questionnaire

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    Summon a Demon and Bind it: A Grounded Theory of LLM Red Teaming in the Wild

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    Engaging in the deliberate generation of abnormal outputs from large language models (LLMs) by attacking them is a novel human activity. This paper presents a thorough exposition of how and why people perform such attacks. Using a formal qualitative methodology, we interviewed dozens of practitioners from a broad range of backgrounds, all contributors to this novel work of attempting to cause LLMs to fail. We relate and connect this activity between its practitioners' motivations and goals; the strategies and techniques they deploy; and the crucial role the community plays. As a result, this paper presents a grounded theory of how and why people attack large language models: LLM red teaming in the wild

    Annotating Online Misogyny

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    How Researchers Manage Ideas

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    The Rumour Mill: Making the Spread of Misinformation Explicit and Tangible

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    Misinformation spread presents a technological and social threat to society. With the advance of AI-based language models, automatically generated texts have become difficult to identify and easy to create at scale. We present "The Rumour Mill", a playful art piece, designed as a commentary on the spread of rumours and automatically-generated misinformation. The mill is a tabletop interactive machine, which invites a user to experience the process of creating believable text by interacting with different tangible controls on the mill. The user manipulates visible parameters to adjust the genre and type of an automatically generated text rumour. The Rumour Mill is a physical demonstration of the state of current technology and its ability to generate and manipulate natural language text, and of the act of starting and spreading rumours.Comment: Accepted to CHI 2020 Interactivit
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