Chan, Stephanie C. Y.

Can language models learn from explanations in context?

Lampinen, A. K., Dasgupta, I., Chan, S. C. Y., Matthewson, K., Tessler, M. H., Creswell, A., McClelland, J. L., Wang, J. X., & Hill, F. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 163

Can language models learn from explanations in context?

Lampinen, A., Dasgupta, I., Chan, S., Mathewson, K., Tessler, M., Creswell, A., McClelland, J., Wang, J., & Hill, F. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022

Topic ModelingNatural Language Processing Techniques

Cited by 142

A probability distribution over latent causes, in the orbitofrontal cortex

Chan, S. C. Y., Niv, Y., & Norman, K. A. (2016) · The Journal of Neuroscience

Neural and Behavioral Psychology StudiesMemory and Neural Mechanisms

Cited by 97

Language models, like humans, show content effects on reasoning tasks

Lampinen, A. K., Dasgupta, I., Chan, S. C. Y., Sheahan, H. R., Creswell, A., Kumaran, D., McClelland, J. L., & Hill, F. (2024) · PNAS Nexus

Logic, Reasoning, and KnowledgeTopic Modeling

Cited by 69

Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Chan, S., Santoro, A., Lampinen, A., Wang, J., Singh, A., Richemond, P., McClelland, J., & Hill, F. (2022) · Advances in Neural Information Processing Systems 35

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 69

Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Chan, S. C. Y., Santoro, A., Lampinen, A. K., Wang, J. X., Singh, A., Richemond, P. H., McClelland, J., & Hill, F. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 69

Measuring the Reliability of Reinforcement Learning Algorithms

Chan, S. C. Y., Fishman, S., Canny, J., Korattikara, A., & Guadarrama, S. (2019) · arXiv

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 57

Language models show human-like content effects on reasoning tasks

Dasgupta, I., Lampinen, A. K., Chan, S. C. Y., Sheahan, H. R., Creswell, A., Kumaran, D., McClelland, J. L., & Hill, F. (2022) · arXiv

Logic, Reasoning, and KnowledgeTopic Modeling

Cited by 53

Many-Shot In-Context Learning

Agarwal, R., Singh, A., Zhang, L. M., Bohnet, B., Rosias, L., Chan, S., Zhang, B., Anand, A., Abbas, Z., Nova, A., et al. (2024) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 35

Lingering representations of stimuli influence recall organization

Chan, S. C. Y., Applegate, M. C., Morton, N. W., Polyn, S. M., & Norman, K. A. (2017) · Neuropsychologia

Memory Processes and InfluencesMemory and Neural Mechanisms

Cited by 28

Tell me why! Explanations support learning relational and causal structure

Lampinen, A. K., Roy, N. A., Dasgupta, I., Chan, S. C. Y., Tam, A. C., McClelland, J. L., Yan, C., Santoro, A., Rabinowitz, N. C., Wang, J. X., et al. (2021) · arXiv

Reinforcement Learning in RoboticsExplainable Artificial Intelligence (XAI)

Cited by 14

Transformers generalize differently from information stored in context vs in weights

Chan, S. C. Y., Dasgupta, I., Kim, J., Kumaran, D., Lampinen, A. K., & Hill, F. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 11

Scaling Instructable Agents Across Many Simulated Worlds

SIMA Team, Raad, M. A., Ahuja, A., Barros, C., Besse, F., Bolt, A., Bolton, A., Brownfield, B., Buttimore, G., Cant, M., et al. (2024) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 9

LearnLM: Improving Gemini for Learning

LearnLM Team, Modi, A., Veerubhotla, A. S., Rysbek, A., Huber, A., Wiltshire, B., Veprek, B., Gillick, D., Kasenberg, D., Ahmed, D., et al. (2024) · arXiv

Intelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Education

Cited by 4