Summary of Dataframe Qa: a Universal Llm Framework on Dataframe Question Answering Without Data Exposure, by Junyi Ye et al.
DataFrame QA: A Universal LLM Framework on DataFrame Question Answering Without Data Exposure
by Junyi Ye, Mengnan Du, Guiling Wang
First submitted to arxiv on: 27 Jan 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 The paper introduces DataFrame question answering (QA), a novel task that utilizes large language models (LLMs) to generate Pandas queries for information retrieval and data analysis on dataframes. The method relies solely on dataframe column names, ensuring data privacy while reducing the context window in the prompt. This approach addresses major challenges in LLM-based data analysis. The paper proposes DataFrame QA as a comprehensive framework that includes safe Pandas query generation and code execution. Various LLMs are evaluated using the pass@1 metric on WikiSQL and UCI-DataFrameQA, with GPT-4 achieving high pass@1 rates. This approach is adaptable and secure for diverse applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to ask a computer questions about data without sharing any personal or sensitive information. It uses special language models that can understand and answer questions about dataframes (like tables of numbers). The researchers developed a new way to ask questions using just the column names, keeping the data private. They tested this method with different language models and found that one model, GPT-4, did very well at answering questions correctly. |
Keywords
» Artificial intelligence » Context window » Gpt » Prompt » Question answering
