ContextMate: a context-aware smart agent for efficient data analysis

Author:

Jadoon Aamir KhanORCID,Yu ChunORCID,Shi YuanchunORCID

Funder

Natural Science Foundation of Beijing Municipality

Beijing Municipal Natural Science Foundation

Guoqiang Institute, Tsinghua University

Science and Technology Innovation 2025 Major Project of Ningbo

Publisher

Springer Science and Business Media LLC

Reference68 articles.

1. Bach, J., Bolton, M.: A Context-Driven Approach to Automation in Testing. Technical report, Satisfice, Inc., Feb. 2016. Available at https://shorturl.at/cORT9 (2016)

2. Bazire, M., Brézillon, P.: Understanding context before using it. In: Modeling and Using Context: 5thInternational and Interdisciplinary Conference CONTEXT 2005, Paris, France, . Proceedings 5, pp. 29–40. Springer (2005)

3. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A.: Language models are few-shot learners. Adv. Neural Inform. Process. Syst. 33, 1877–1901 (2020)

4. Chase, H.: Langchain (2023). https://python.langchain.com/en/latest/index.html

5. Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.d.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G.: Evaluating large language models trained on code. arXiv preprint https://arxiv.org/abs/2107.03374 (2021)

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