Zobrazeno 1 - 10
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pro vyhledávání: '"İnan, Mert"'
Effective human-machine collaboration requires machine learning models to externalize uncertainty, so users can reflect and intervene when necessary. For language models, these representations of uncertainty may be impacted by sycophancy bias: procli
Externí odkaz:
http://arxiv.org/abs/2410.14746
Autor:
Cheng, Qi, İnan, Mert, Mbarki, Rahma, Grmek, Grace, Choi, Theresa, Sun, Yiming, Persaud, Kimele, Wang, Jenny, Alikhani, Malihe
Understanding uncertainty plays a critical role in achieving common ground (Clark et al.,1983). This is especially important for multimodal AI systems that collaborate with users to solve a problem or guide the user through a challenging concept. In
Externí odkaz:
http://arxiv.org/abs/2410.14050
We introduce a goal-oriented conversational AI system enhanced with American Sign Language (ASL) instructions, presenting the first implementation of such a system on a worldwide multimodal conversational AI platform. Accessible through a touch-based
Externí odkaz:
http://arxiv.org/abs/2410.14026
Autor:
Kennington, Casey, Alikhani, Malihe, Pon-Barry, Heather, Atwell, Katherine, Bisk, Yonatan, Fried, Daniel, Gervits, Felix, Han, Zhao, Inan, Mert, Johnston, Michael, Korpan, Raj, Litman, Diane, Marge, Matthew, Matuszek, Cynthia, Mead, Ross, Mohan, Shiwali, Mooney, Raymond, Parde, Natalie, Sinapov, Jivko, Stewart, Angela, Stone, Matthew, Tellex, Stefanie, Williams, Tom
The ability to interact with machines using natural human language is becoming not just commonplace, but expected. The next step is not just text interfaces, but speech interfaces and not just with computers, but with all machines including robots. I
Externí odkaz:
http://arxiv.org/abs/2404.01158
Addressing the critical shortage of mental health resources for effective screening, diagnosis, and treatment remains a significant challenge. This scarcity underscores the need for innovative solutions, particularly in enhancing the accessibility an
Externí odkaz:
http://arxiv.org/abs/2402.08837
Task planning is an important component of traditional robotics systems enabling robots to compose fine grained skills to perform more complex tasks. Recent work building systems for translating natural language to executable actions for task complet
Externí odkaz:
http://arxiv.org/abs/2305.06485
Autor:
Alikhani, Malihe, Kober, Thomas, Alhafni, Bashar, Chen, Yue, Inan, Mert, Nielsen, Elizabeth, Raji, Shahab, Steedman, Mark, Stone, Matthew
Typologically diverse languages offer systems of lexical and grammatical aspect that allow speakers to focus on facets of event structure in ways that comport with the specific communicative setting and discourse constraints they face. In this paper,
Externí odkaz:
http://arxiv.org/abs/2207.02356
End-to-end sign language generation models do not accurately represent the prosody in sign language. A lack of temporal and spatial variations leads to poor-quality generated presentations that confuse human interpreters. In this paper, we aim to imp
Externí odkaz:
http://arxiv.org/abs/2203.09679
State-of-the-art sign language generation frameworks lack expressivity and naturalness which is the result of only focusing manual signs, neglecting the affective, grammatical and semantic functions of facial expressions. The purpose of this work is
Externí odkaz:
http://arxiv.org/abs/2202.05383
Developers of text generation models rely on automated evaluation metrics as a stand-in for slow and expensive manual evaluations. However, image captioning metrics have struggled to give accurate learned estimates of the semantic and pragmatic succe
Externí odkaz:
http://arxiv.org/abs/2109.05281