Fetching the paper…
Reading the bibliography…
Researchers and practitioners have recently reframed powerful Large Language Models (LLMs) as agents, enabling them to automate complex tasks largely via the use of specialized functions.
Probability inequalities for sums of bounded random variables
Hoeffding, W · 1994
Earlier work this paper cites.
Software complexity, crosstalk
McCabe, T. J · 1994
Earlier work this paper cites.
Rubber hands ‘feel’touch that eyes see
Botvinick, M. and Cohen, J · 1998
Earlier work this paper cites.
An overview of gradient descent optimization algorithms
Ruder, S · 2016
Earlier work this paper cites.
World of bits: An open-domain platform for web-based agents
Shi, T., Karpathy, A., Fan, L., Hernandez, J., and Liang, P · 2017
Earlier work this paper cites.
Hyperband: A novel bandit-based approach to hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A · 2018
Earlier work this paper cites.
Revisiting small batch training for deep neural networks
Masters, D. and Luschi, C · 2018
Earlier work this paper cites.
” hello ai”: uncovering the onboarding needs of medical practitioners for human-ai collaborative decision-making
Cai, C. J., Winter, S., Steiner, D., Wilcox, L., and Terry, M · 2019
Earlier work this paper cites.
Measuring mathematical problem solving with the MATH dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J · 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.
Frugal optimization for cost-related hyperparameters
Wu, Q., Wang, C., and Huang, S · 2021
Earlier work this paper cites.
A data-driven approach for learning to control computers
Humphreys, P. C., Raposo, D., Pohlen, T., Thornton, G., Chhaparia, R., Muldal, A., Abramson, J., Georgiev, P., Santoro, A., and Lillicrap, T · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
Earlier work this paper cites.
Targeted hyperparameter optimization with lexicographic preferences over multiple objectives
Zhang, S., Jia, F., Wang, C., and Wu, Q · 2022
Earlier work this paper cites.
Domain generalization: A survey
Zhou, K., Liu, Z., Qiao, Y., Xiang, T., and Loy, C. C · 2022
Cited alongside, same era.
Github — babyagi
BabyAGI · 2023
Cited alongside, same era.
An exploratory survey about using chatgpt in education, healthcare, and research
Hosseini, M., Gao, C. A., Liebovitz, D. M., Carvalho, A. M., Ahmad, F. S., Luo, Y., MacDonald, N., Holmes, K. L., and Kho, A · 2023
Cited alongside, same era.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
Cited alongside, same era.
Camel: Communicative agents for” mind” exploration of large language model society
Li, G., Hammoud, H. A. A. K., Itani, H., Khizbullin, D., and Ghanem, B · 2023
Cited alongside, same era.
Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning
Tool learning with foundation models
Qin, Y., Hu, S., Lin, Y., Chen, W., Ding, N., Cui, G., Zeng, Z., Huang, Y., Xiao, C., Han, C., et al · 2023
Later among the works it cites.
Code llama: Open foundation models for code
Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Remez, T., Rapin, J., et al · 2023
Later among the works it cites.
Evil geniuses: Delving into the safety of llm-based agents
Tian, Y., Yang, X., Zhang, J., Dong, Y., and Su, H · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 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…
Lu, P., Qiu, L., Chang, K.-W., Wu, Y. N., Zhu, S.-C., Rajpurohit, T., Clark, P., and Kalyan, A · 2023
Cited alongside, same era.
Gaia: a benchmark for general ai assistants
Mialon, G., Fourrier, C., Swift, C., Wolf, T., LeCun, Y., and Scialom, T · 2023
Cited alongside, same era.
Biases in large language models: Origins, inventory and discussion
Navigli, R., Conia, S., and Ross, B · 2023
Cited alongside, same era.
Introducing ChatGPT, 2022
OpenAI · 2023
Cited alongside, same era.
Gpt-4 technical report, 2023
OpenAI · 2023
Cited alongside, same era.
Generative agents: Interactive simulacra of human behavior
Park, J. S., O’Brien, J., Cai, C. J., Morris, M. R., Liang, P., and Bernstein, M. S · 2023
Cited alongside, same era.
Gorilla: Large language model connected with massive apis
Patil, S. G., Zhang, T., Wang, X., and Gonzalez, J. E · 2023
Cited alongside, same era.
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al · 2023
Later among the works it cites.
React: Synergizing reasoning and acting in language models
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y · 2023
Later among the works it cites.
Using large language models for hyperparameter optimization
Zhang, M. R., Desai, N., Bae, J., Lorraine, J., and Ba, J · 2023
Later among the works it cites.
Large language models as tool makers
Cai, T., Wang, X., Ma, T., Chen, X., and Zhou, D · 2024
Closest in time.
Metagpt: Meta programming for multi-agent collaborative framework
Hong, S., Zheng, X., Chen, J., Cheng, Y., Wang, J., Zhang, C., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., et al · 2024
Closest in time.
Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., Casas, D. d. l., Hanna, E. B., Bressand, F., et al · 2024
Closest in time.
An empirical study of the code generation of safety-critical software using llms
Liu, M., Wang, J., Lin, T., Ma, Q., Fang, Z., and Wu, Y · 2024
Closest in time.
Toolllm: Facilitating large language models to master 16000+ real-world apis
Qin, Y., Liang, S., Ye, Y., Zhu, K., Yan, L., Lu, Y., Lin, Y., Cong, X., Tang, X., Qian, B., et al · 2024
Closest in time.
Large language models as optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X · 2024
Closest in time.
Craft: Customizing llms by creating and retrieving from specialized toolsets
Yuan, L., Chen, Y., Wang, X., Fung, Y. R., Peng, H., and Ji, H · 2024
Closest in time.