Andreas, Jacob

Speaker-Follower Models for Vision-and-Language Navigation

Fried, D., Hu, R., Cirik, V., Rohrbach, A., Andreas, J., Morency, L.-P., Berg-Kirkpatrick, T., Saenko, K., Klein, D., & Darrell, T. (2018) · arXiv

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 291

Modular Multitask Reinforcement Learning with Policy Sketches

Andreas, J., Klein, D., & Levine, S. (2016) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 274

Reasoning about Pragmatics with Neural Listeners and Speakers

Andreas, J., & Klein, D. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Multimodal Machine Learning ApplicationsSpeech and dialogue systems

Cited by 115

Task-Oriented Dialogue as Dataflow Synthesis

Andreas, J., Bufe, J., Burkett, D., Chen, C., Clausman, J., Crawford, J., Crim, K., DeLoach, J., Dorner, L., Eisner, J., et al. (2020) · Transactions of the Association for Computational Linguistics

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 98

Linearity of Relation Decoding in Transformer Language Models

Hernandez, E., Sharma, A. S., Haklay, T., Meng, K., Wattenberg, M., Andreas, J., Belinkov, Y., & Bau, D. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 98

Language Models as Agent Models

Andreas, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022

Natural Language Processing TechniquesTopic Modeling

Cited by 92

Language Models as Agent Models

Andreas, J. (2022) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 92

What learning algorithm is in-context learning? Investigations with linear models

Akyürek, E., Schuurmans, D., Andreas, J., Ma, T., & Zhou, D. (2022) · arXiv

Domain Adaptation and Few-Shot LearningNeural Networks and Applications

Cited by 86

Implicit Representations of Meaning in Neural Language Models

Li, B. Z., Nye, M., & Andreas, J. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 84

Unified Pragmatic Models for Generating and Following Instructions

Fried, D., Andreas, J., & Klein, D. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 64

Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks

Wu, Z., Qiu, L., Ross, A., Akyürek, E., Chen, B., Wang, B., Kim, N., Andreas, J., & Kim, Y. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 58

Skill Induction and Planning with Latent Language

Sharma, P., Torralba, A., & Andreas, J. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

AI-based Problem Solving and PlanningReinforcement Learning in Robotics

Cited by 39

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought

Wong, L., Grand, G., Lew, A. K., Goodman, N. D., Mansinghka, V. K., Andreas, J., & Tenenbaum, J. B. (2023) · arXiv

Logic, Reasoning, and KnowledgeNatural Language Processing Techniques

Cited by 36

Are Visual Explanations Useful? A Case Study in Model-in-the-Loop Prediction

Chu, E., Roy, D., & Andreas, J. (2020) · arXiv

Explainable Artificial Intelligence (XAI)Human-Automation Interaction and Safety

Cited by 34

Language Models Trained on Media Diets Can Predict Public Opinion

Chu, E., Andreas, J., Ansolabehere, S., & Roy, D. (2023) · arXiv

Sentiment Analysis and Opinion MiningOpinion Dynamics and Social Influence

Cited by 24

Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks

Wu, Z., Qiu, L., Ross, A., Akyürek, E., Chen, B., Wang, B., Kim, N., Andreas, J., & Kim, Y. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 19

The clock and the pizza: Two stories in mechanistic explanation of neural networks

Zhong, Z., Liu, Z., Tegmark, M., & Andreas, J. (2023) · arXiv

Philosophy of Biology and Scientific ExplanationPhilosophy and Theoretical Science

Cited by 14

Generative AI in the Era of 'Alternative Facts'

Gabriel, S., Lyu, L., Siderius, J., Ghassemi, M., Andreas, J., & Ozdaglar, A. (2024) · An MIT Exploration of Generative AI

Misinformation and Its ImpactsSpam and Phishing Detection

Cited by 11

Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability

Akyürek, A. F., Akyürek, E., Choshen, L., Wijaya, D., & Andreas, J. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4

The Surprising Effectiveness of Test-Time Training for Few-Shot Learning

Akyürek, E., Damani, M., Zweiger, A., Qiu, L., Guo, H., Pari, J., Kim, Y., & Andreas, J. (2024) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 4

In-Context Language Learning: Architectures and Algorithms

Akyürek, E., Wang, B., Kim, Y., & Andreas, J. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 3

LILO: Learning Interpretable Libraries by Compressing and Documenting Code

Grand, G., Wong, L., Bowers, M., Olausson, T. X., Liu, M., Tenenbaum, J. B., & Andreas, J. (2023) · arXiv

Software Engineering ResearchEvolutionary Algorithms and Applications

Cited by 2

Elements of World Knowledge (EWOK): A cognition-inspired framework for evaluating basic world knowledge in language models

Ivanova, A. A., Sathe, A., Lipkin, B., Kumar, U., Radkani, S., Clark, T. H., Kauf, C., Hu, J., Pramod, R. T., Grand, G., et al. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 2

ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning

Hou, B., Zhang, Y., Ji, J., Liu, Y., Qian, K., Andreas, J., & Chang, S. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0