Hill, Felix

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Topic ModelingNatural Language Processing Techniques

Cited by 4,222

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,051

Learning Distributed Representations of Sentences from Unlabelled Data

Hill, F., Cho, K., & Korhonen, A. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 566

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2018) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 562

Multimodal Few-Shot Learning with Frozen Language Models

Tsimpoukelli, M., Menick, J., Cabi, S., Eslami, S. M. A., Vinyals, O., & Hill, F. (2021) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 355

Analysing Mathematical Reasoning Abilities of Neural Models

Saxton, D., Grefenstette, E., Hill, F., & Kohli, P. (2019) · arXiv

Topic ModelingMathematics, Computing, and Information Processing

Cited by 241

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

Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models

McClelland, J. L., Hill, F., Rudolph, M., Baldridge, J., & Schütze, H. (2020) · Proceedings of the National Academy of Sciences

Neurobiology of Language and BilingualismTopic Modeling

Cited by 91

HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment

Vulić, I., Gerz, D., Kiela, D., Hill, F., & Korhonen, A. (2016) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 90

Environmental drivers of systematicity and generalization in a situated agent

Hill, F., Lampinen, A., Schneider, R., Clark, S., Botvinick, M., McClelland, J. L., & Santoro, A. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 81

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

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

Imitating Interactive Intelligence

Abramson, J., Ahuja, A., Barr, I., Brussee, A., Carnevale, F., Cassin, M., Chhaparia, R., Clark, S., Damoc, B., Dudzik, A., et al. (2020) · arXiv

Reinforcement Learning in RoboticsSocial Robot Interaction and HRI

Cited by 43

Grounded Language Learning Fast and Slow

Hill, F., Tieleman, O., von Glehn, T., Wong, N., Merzic, H., & Clark, S. (2020) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 35

Semantic Exploration from Language Abstractions and Pretrained Representations

Tam, A. C., Rabinowitz, N. C., Lampinen, A. K., Roy, N. A., Chan, S. C. Y., Strouse, D., Wang, J. X., Banino, A., & Hill, F. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 24

Collaborating with language models for embodied reasoning

Dasgupta, I., Kaeser-Chen, C., Marino, K., Ahuja, A., Babayan, S., Hill, F., & Fergus, R. (2023) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 14

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