Large Language Models as your Personal Data Scientist
Abstract: Large Language Models (LLMs) have contributed to massive performance improvements for various language understanding and generation tasks; however, their limits are yet to be fully explored for "ill-defined" complex tasks. One such task is conversational data science, where a user can talk to an intelligent agent to explain their data science needs, and the agent will serve the user by engaging in a conversation with them like any human data science would do and, accordingly, formulate and execute precise Machine Learning tasks. Although this is a very ambitious goal, given the recent developments in LLMs, a fully functional conversational data science system seems quite achievable in the near future. Through an in-depth case study in this paper, we delved into the potential of employing LLMs as a solution to conversational data science. We hope that our findings will not only broaden the horizons of NLP research but also bring transformative changes in future AI technology.
Submission Length: Regular submission (no more than 12 pages of main content)
Previous TMLR Submission Url: https://openreview.net/forum?id=47RT2lovOh
Changes Since Last Submission: The font has been changed to default font of TMLR.
Assigned Action Editor: ~Yang_Li2
Submission Number: 1520
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