Rocktäschel, Tim

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv

Safety Systems Engineering in AutonomyEthics and Social Impacts of AI

Cited by 3,101

Language Models as Knowledge Bases?

Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., & Miller, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 1,834

e-SNLI: Natural language inference with natural language explanations

Camburu, O.-M., Rocktäschel, T., Lukasiewicz, T., & Blunsom, P. (2018) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 367

KILT: a Benchmark for Knowledge Intensive Language Tasks

Petroni, F., Piktus, A., Fan, A., Lewis, P., Yazdani, M., De Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingNatural Language Processing Techniques

Cited by 357

A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

Kirk, R., Zhang, A., Grefenstette, E., & Rocktäschel, T. (2023) · Journal of Artificial Intelligence Research

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 193

How Context Affects Language Models' Factual Predictions

Petroni, F., Lewis, P., Piktus, A., Rocktäschel, T., Wu, Y., Miller, A. H., & Riedel, S. (2020) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 154

Interpretation of Natural Language Rules in Conversational Machine Reading

Saeidi, M., Bartolo, M., Lewis, P., Singh, S., Rocktäschel, T., Sheldon, M., Bouchard, G., & Riedel, S. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 141

The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs

Ruis, L., Khan, A., Biderman, S., Hooker, S., Rocktäschel, T., & Grefenstette, E. (2023) · Advances in Neural Information Processing Systems 36

Natural Language Processing TechniquesSyntax, Semantics, Linguistic Variation

Cited by 47

The NetHack Learning Environment

Küttler, H., Nardelli, N., Miller, A. H., Raileanu, R., Selvatici, M., Grefenstette, E., & Rocktäschel, T. (2020) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 44

Prioritized Level Replay

Jiang, M., Grefenstette, E., & Rocktäschel, T. (2020) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 33

Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution

Fernando, C., Banarse, D., Michalewski, H., Osindero, S., & Rocktäschel, T. (2023) · arXiv

Topic ModelingEvolutionary Algorithms and Applications

Cited by 31

Evolving Curricula with Regret-Based Environment Design

Parker-Holder, J., Jiang, M., Dennis, M., Samvelyan, M., Foerster, J., Grefenstette, E., & Rocktäschel, T. (2022) · arXiv

Reinforcement Learning in RoboticsEvolutionary Algorithms and Applications

Cited by 28

Genie: Generative Interactive Environments

Bruce, J., Dennis, M., Edwards, A., Parker-Holder, J., Shi, Y., Hughes, E., Lai, M., Mavalankar, A., Steigerwald, R., Apps, C., et al. (2024) · arXiv

Reinforcement Learning in RoboticsGenerative Adversarial Networks and Image Synthesis

Cited by 23

Exploration via Elliptical Episodic Bonuses

Henaff, M., Raileanu, R., Jiang, M., & Rocktäschel, T. (2022) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 22

Replay-Guided Adversarial Environment Design

Jiang, M., Dennis, M., Parker-Holder, J., Foerster, J., Grefenstette, E., & Rocktäschel, T. (2021) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 21

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

Samvelyan, M., Kirk, R., Kurin, V., Parker-Holder, J., Jiang, M., Hambro, E., Petroni, F., Küttler, H., Grefenstette, E., & Rocktäschel, T. (2021) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 20

Human-Timescale Adaptation in an Open-Ended Task Space

Adaptive Agent Team, Bauer, J., Baumli, K., Baveja, S., Behbahani, F., Bhoopchand, A., Bradley-Schmieg, N., Chang, M., Clay, N., Collister, A., et al. (2023) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 19

General intelligence requires rethinking exploration

Jiang, M., Rocktäschel, T., & Grefenstette, E. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 17

Debating with More Persuasive LLMs Leads to More Truthful Answers

Khan, A., Hughes, J., Valentine, D., Ruis, L., Sachan, K., Radhakrishnan, A., Grefenstette, E., Bowman, S. R., Rocktäschel, T., & Perez, E. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 13

MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning

Samvelyan, M., Khan, A., Dennis, M., Jiang, M., Parker-Holder, J., Foerster, J., Raileanu, R., & Rocktäschel, T. (2023) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 8

BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games

Paglieri, D., Cupiał, B., Coward, S., Piterbarg, U., Wolczyk, M., Khan, A., Pignatelli, E., Kuciński, Ł., Pinto, L., Fergus, R., et al. (2024) · arXiv

Artificial Intelligence in GamesMultimodal Machine Learning Applications

Cited by 2

Insights From the NeurIPS 2021 NetHack Challenge

Hambro, E., Mohanty, S., Babaev, D., Byeon, M., Chakraborty, D., Grefenstette, E., Jiang, M., Jo, D., Kanervisto, A., Kim, J., et al. (2022) · arXiv

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 1

Stabilizing Unsupervised Environment Design with a Learned Adversary

Mediratta, I., Jiang, M., Parker-Holder, J., Dennis, M., Vinitsky, E., & Rocktäschel, T. (2023) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 1

TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation

Cook, J., Rocktäschel, T., Foerster, J., Aumiller, D., & Wang, A. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0