Lampinen, Andrew K.

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

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

Integration of new information in memory: new insights from a complementary learning systems perspective

McClelland, J. L., McNaughton, B. L., & Lampinen, A. K. (2020) · Philosophical Transactions of the Royal Society B: Biological Sciences

Memory and Neural MechanismsMemory Processes and Influences

Cited by 136

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

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

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

Getting aligned on representational alignment

Sucholutsky, I., Muttenthaler, L., Weller, A., Peng, A., Bobu, A., Kim, B., Love, B. C., Cueva, C. J., Grant, E., Groen, I., et al. (2023) · arXiv

Domain Adaptation and Few-Shot LearningExplainable Artificial Intelligence (XAI)

Cited by 36

Can language models handle recursively nested grammatical structures? A case study on comparing models and humans

Lampinen, A. K. (2022) · arXiv

Natural Language Processing TechniquesSyntax, Semantics, Linguistic Variation

Cited by 32

Can Language Models Handle Recursively Nested Grammatical Structures? A Case Study on Comparing Models and Humans

Lampinen, A. (2024) · Computational Linguistics

Natural Language Processing TechniquesSyntax, Semantics, Linguistic Variation

Cited by 32

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

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

Evaluating Spatial Understanding of Large Language Models

Yamada, Y., Bao, Y., Lampinen, A. K., Kasai, J., & Yildirim, I. (2023) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 8

Symbol tuning improves in-context learning in language models

Wei, J., Hou, L., Lampinen, A., Chen, X., Huang, D., Tay, Y., Chen, X., Lu, Y., Zhou, D., Ma, T., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 1

On the generalization of language models from in-context learning and finetuning: a controlled study

Lampinen, A. K., Chaudhry, A., Chan, S. C. Y., Wild, C., Wan, D., Ku, A., Bornschein, J., Pascanu, R., Shanahan, M., & McClelland, J. L. (2025) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 0