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pro vyhledávání: '"Ong, Desmond C."'
Autor:
Gandhi, Kanishk, Lynch, Zoe, Fränken, Jan-Philipp, Patterson, Kayla, Wambu, Sharon, Gerstenberg, Tobias, Ong, Desmond C., Goodman, Noah D.
Understanding emotions is fundamental to human interaction and experience. Humans easily infer emotions from situations or facial expressions, situations from emotions, and do a variety of other affective cognition. How adept is modern AI at these in
Externí odkaz:
http://arxiv.org/abs/2409.11733
Autor:
Ong, Desmond C.
Large Language Models have taken the cognitive science world by storm. It is perhaps timely now to take stock of the various research paradigms that have been used to make scientific inferences about ``cognition" in these models or about human cognit
Externí odkaz:
http://arxiv.org/abs/2406.09464
Large language models (LLMs) have offered new opportunities for emotional support, and recent work has shown that they can produce empathic responses to people in distress. However, long-term mental well-being requires emotional self-regulation, wher
Externí odkaz:
http://arxiv.org/abs/2404.01288
Large Language Models (LLMs) have demonstrated surprising performance on many tasks, including writing supportive messages that display empathy. Here, we had these models generate empathic messages in response to posts describing common life experien
Externí odkaz:
http://arxiv.org/abs/2403.18148
The emotions we experience involve complex processes; besides physiological aspects, research in psychology has studied cognitive appraisals where people assess their situations subjectively, according to their own values (Scherer, 2005). Thus, the s
Externí odkaz:
http://arxiv.org/abs/2310.14389
Autor:
Suresh, Varsha, Ong, Desmond C.
Publikováno v:
10th International Conference on Affective Computing and Intelligent Interaction (ACII), 2022
Machine learning models automatically learn discriminative features from the data, and are therefore susceptible to learn strongly-correlated biases, such as using protected attributes like gender and race. Most existing bias mitigation approaches ai
Externí odkaz:
http://arxiv.org/abs/2303.04896
Publikováno v:
NeurIPS 2021 (Dataset and Benchmarks Track)
The ability to compositionally map language to referents, relations, and actions is an essential component of language understanding. The recent gSCAN dataset (Ruis et al. 2020, NeurIPS) is an inspiring attempt to assess the capacity of models to lea
Externí odkaz:
http://arxiv.org/abs/2109.08994
Autor:
Suresh, Varsha, Ong, Desmond C.
Fine-grained classification involves dealing with datasets with larger number of classes with subtle differences between them. Guiding the model to focus on differentiating dimensions between these commonly confusable classes is key to improving perf
Externí odkaz:
http://arxiv.org/abs/2109.05427
Autor:
Suresh, Varsha, Ong, Desmond C.
Modern emotion recognition systems are trained to recognize only a small set of emotions, and hence fail to capture the broad spectrum of emotions people experience and express in daily life. In order to engage in more empathetic interactions, future
Externí odkaz:
http://arxiv.org/abs/2108.00194
Autor:
Ong, Desmond C.
The recent rapid advancements in artificial intelligence research and deployment have sparked more discussion about the potential ramifications of socially- and emotionally-intelligent AI. The question is not if research can produce such affectively-
Externí odkaz:
http://arxiv.org/abs/2107.13734