2023 · 82 citations · 26 references
ComputingCommunicationComputer ScienceArtificial IntelligenceHuman-computer InteractionNatural Language ProcessingAlternative CommunicationAlternative Communication SystemsAugmentative CommunicationConversational InterfacesConversational User InterfacesLanguage ModelingAac InterventionAlternative Communication DevicesAlternative Communication InterventionCommunication Style DifferencesConversational User ModelingLanguage Model PromptingLarge Language ModelsPrompting
Users of augmentative and alternative communication (AAC) devices sometimes find it difficult to communicate in real time with others due to the time it takes to compose messages. AI technologies such as large language models (LLMs) provide an opportunity to support AAC users by improving the quality and variety of text suggestions. However, these technologies may fundamentally change how users interact with AAC devices as users transition from typing their own phrases to prompting and selecting AI-generated phrases. We conducted a study in which 12 AAC users tested live suggestions from a language model across three usage scenarios: extending short replies, answering biographical questions, and requesting assistance. Our study participants believed that AI-generated phrases could save time, physical and cognitive effort when communicating, but felt it was important that these phrases reflect their own communication style and preferences. This work identifies opportunities and challenges for future AI-enhanced AAC devices.
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LaMDA: Language Models for Dialog Applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall et al. · arXiv (Cornell University) · 2022 · 706 citations · Full text
Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design
Qian Yang, Aaron Steinfeld, Carolyn Penstein Rosé et al. · 2020 · 573 citations · Full text
Computing, Computer Science, Design +13
I Lead, You Help but Only with Enough Details
Changhoon Oh, Jungwoo Song, Jinhan Choi et al. · 2018 · 291 citations