Summary of Dataagent: Evaluating Large Language Models’ Ability to Answer Zero-shot, Natural Language Queries, by Manit Mishra et al.
DataAgent: Evaluating Large Language Models’ Ability to Answer Zero-Shot, Natural Language Queries
by Manit Mishra, Abderrahman Braham, Charles Marsom, Bryan Chung, Gavin Griffin, Dakshesh Sidnerlikar, Chatanya Sarin, Arjun Rajaram
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper evaluates OpenAI’s GPT-3.5 as a “Language Data Scientist” (LDS) capable of extracting meaningful information from datasets without manual coding and data collection. The model is tested on diverse benchmark datasets to assess its performance across various data science tasks, including code-generation using libraries like NumPy, Pandas, Scikit-Learn, and TensorFlow. The LDS employs novel prompt engineering techniques, such as Chain-of-Thought reinforcement and SayCan prompt engineering, to answer complex questions related to the benchmark datasets with great accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way for machines to analyze data and find important information without needing human help. GPT-3.5 is tested on different sets of data to see if it can do this accurately. The results show that GPT-3.5 is good at finding answers to complex questions about the data, which could be very helpful for people who work with data. |
Keywords
» Artificial intelligence » Gpt » Prompt
