Baba Is AI: Break the Rules to Beat the Benchmark

Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks

Lake, B. M. and Baroni, M · 2017

Earlier work this paper cites.

Building machines that learn and think like people

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J · 2017

Earlier work this paper cites.

Language models are few-shot learners

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020

Earlier work this paper cites.

A benchmark for systematic generalization in grounded language understanding

Ruis, L., Andreas, J., Baroni, M., Bouchacourt, D., and Lake, B. M · 2020

Earlier work this paper cites.

Language models as zero-shot planners: Extracting actionable knowledge for embodied agents, 2022

Huang, W., Abbeel, P., Pathak, D., and Mordatch, I · 2022

Earlier work this paper cites.

Do as i can, not as i say: Grounding language in robotic affordances

Ichter, B., Brohan, A., Chebotar, Y., Finn, C., Hausman, K., Herzog, A., Ho, D., Ibarz, J., Irpan, A., Jang, E., Julian, R., Kalashnikov, D., Levine, S., Lu, Y., Parada, C., Rao, K., Sermanet, P., Toshev, A. T., Vanhoucke, V., Xia, F., Xiao, T., Xu, P., Yan, M., Brown, N., Ahn, M., Cortes, O., Sievers, N., Tan, C., Xu, S., Reyes, D., Rettinghouse, J., Quiambao, J., Pastor, P., Luu, L., Lee, K.-H., Kuang, Y., Jesmonth, S., Jeffrey, K., Ruano, R. J., Hsu, J., Gopalakrishnan, K., David, B., Zeng, A., and Fu, C. K · 2022

Cited alongside, same era.

Grounding large language models in interactive environments with online reinforcement learning

Carta, T., Romac, C., Wolf, T., Lamprier, S., Sigaud, O., and Oudeyer, P.-Y · 2023

Cited alongside, same era.

Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks

Original

Chevalier-Boisvert, M., Dai, B., Towers, M., de Lazcano, R., Willems, L., Lahlou, S., Pal, S., Castro, P. S., and Terry, J · 2023

Cited alongside, same era.

Evaluating cognitive maps and planning in large language models with cogeval

Momennejad, I., Hasanbeig, H., Vieira Frujeri, F., Sharma, H., Jojic, N., Palangi, H., Ness, R., and Larson, J · 2023

Cited alongside, same era.

Llm-planner: Few-shot grounded planning for embodied agents with large language models

Song, C., Sadler, B. M., Wu, J., Chao, W., Washington, C., and Su, Y · 2023

Later among the works it cites.

React: Synergizing reasoning and acting in language models, 2023

Original

Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y · 2023

Later among the works it cites.

Chatbot arena: An open platform for evaluating llms by human preference

Original

Chiang, W.-L., Zheng, L., Sheng, Y., Angelopoulos, A. N., Li, T., Li, D., Zhang, H., Zhu, B., Jordan, M., Gonzalez, J. E., and Stoica, I · 2024

Closest in time.

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context, 2024

Team, G · 2024

Closest in time.

Voyager: An open-ended embodied agent with large language models

Wang, G., Xie, Y., Jiang, Y., Mandlekar, A., Xiao, C., Zhu, Y., Fan, L., and Anandkumar, A · 2024