Fetching the paper…
Reading the bibliography…
Owing to recent advancements, Large Language Models (LLMs) can now be deployed as agents for increasingly complex decision-making applications in areas including robotics, gaming, and API integration.
Textworld: A learning environment for text-based games
Côté, M.-A., Ákos Kádár, Yuan, X., Kybartas, B., Barnes, T., Fine, E., Moore, J., Tao, R. Y., Hausknecht, M., Asri, L. E., Adada, M., Tay, W., and Trischler, A · 2018
Earlier work this paper cites.
Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Sharma, P., Ding, N., Goodman, S., and Soricut, R · 2018
Earlier work this paper cites.
Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Gupta, A., Kumar, V., Lynch, C., Levine, S., and Hausman, K · 2019
Earlier work this paper cites.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N. and Gurevych, I · 2019
Earlier work this paper cites.
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., Riedel, S., and Kiela, D · 2020
Earlier work this paper cites.
What makes good in-context examples for gpt- 3 3 ?
Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., and Chen, W · 2021
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
Earlier work this paper cites.
Alfworld: Aligning text and embodied environments for interactive learning
Shridhar, M., Yuan, X., Côté, M.-A., Bisk, Y., Trischler, A., and Hausknecht, M · 2021
Earlier work this paper cites.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning, 2021
Yu, T., Quillen, D., He, Z., Julian, R., Narayan, A., Shively, H., Bellathur, A., Hausman, K., Finn, C., and Levine, S · 2021
Cited alongside, same era.
Memory-assisted prompt editing to improve gpt-3 after deployment
Madaan, A., Tandon, N., Clark, P., and Yang, Y · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P. F., Leike, J., and Lowe, R · 2022
Cited alongside, same era.
Selective annotation makes language models better few-shot learners
Su, H., Kasai, J., Wu, C. H., Shi, W., Wang, T., Xin, J., Zhang, R., Ostendorf, M., Zettlemoyer, L., Smith, N. A., and Yu, T · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., ichter, b., Xia, F., Chi, E., Le, Q. V., and Zhou, D · 2022
Cited alongside, same era.
Large language models as general pattern machines
Mirchandani, S., Xia, F., Florence, P., Ichter, B., Driess, D., Arenas, M. G., Rao, K., Sadigh, D., and Zeng, A · 2023
Later among the works it cites.
Gpt-4 technical report
OpenAI · 2023
Later among the works it cites.
Adapt: As-needed decomposition and planning with language models
Prasad, A., Koller, A., Hartmann, M., Clark, P., Sabharwal, A., Bansal, M., and Khot, T · 2023
Later among the works it cites.
Reflexion: Language agents with verbal reinforcement learning
Shinn, N., Cassano, F., Berman, E., Gopinath, A., Narasimhan, K., and Yao, S · 2023
Later among the works it cites.
The rise and potential of large language model based agents: A survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., Zheng, R., Fan, X., Wang, X., Xiong, L., Zhou, Y., Wang, W., Jiang, C., Zou, Y., Liu, X., Yin, Z., Dou, S., Weng, R., Cheng, W., Zhang, Q., Qin, W., Zheng, Y., Qiu, X., Huang, X., and Gui, T · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Webshop: Towards scalable real-world web interaction with grounded language agents
Yao, S., Chen, H., Yang, J., and Narasimhan, K · 2022
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models
GenAI, M · 2023
Cited alongside, same era.
Liu, H., Li, C., Wu, Q., and Lee, Y. J · 2023
Cited alongside, same era.
A survey on large language model based autonomous agents
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., Zhao, W. X., Wei, Z., and Wen, J.-R
Cited in the paper.
A Survey on Large Language Model based Autonomous Agents
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., Zhao, W. X., Wei, Z., and Wen, J.-R
Cited in the paper.
CogVLM: Visual Expert for Pretrained Language Models
Wang, W., Lv, Q., Yu, W., Hong, W., Qi, J., Wang, Y., Ji, J., Yang, Z., Zhao, L., Song, X., Xu, J., Xu, B., Li, J., Dong, Y., Ding, M., and Tang, J
Cited in the paper.
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y · 2023
Later among the works it cites.
A survey on multimodal large language models
Yin, S., Fu, C., Zhao, S., Li, K., Sun, X., Xu, T., and Chen, E · 2023
Later among the works it cites.
ExpeL: LLM Agents Are Experiential Learners
Zhao, A., Huang, D., Xu, Q., Lin, M., Liu, Y.-J., and Huang, G · 2023
Later among the works it cites.