Summary of Tapilot-crossing: Benchmarking and Evolving Llms Towards Interactive Data Analysis Agents, by Jinyang Li et al.
Tapilot-Crossing: Benchmarking and Evolving LLMs Towards Interactive Data Analysis Agents
by Jinyang Li, Nan Huo, Yan Gao, Jiayi Shi, Yingxiu Zhao, Ge Qu, Yurong Wu, Chenhao Ma, Jian-Guang Lou, Reynold Cheng
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 introduces Tapilot-Crossing, a new benchmark for evaluating Large Language Model (LLM) agents in interactive data analysis tasks. The benchmark contains 1024 interactions across four practical scenarios, and is constructed through the Decision Company multi-agent environment with minimal human effort. The authors evaluate popular and advanced LLM agents on this benchmark, highlighting the challenges of interactive data analysis. They also propose Adaptive Interaction Reflection (AIR), a self-generated reflection strategy that guides LLM agents to learn from successful history. Experiments demonstrate that AIR can improve LLM performance in interactive data analysis by up to 44.5%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers and humans work together better for decision-making. It’s hard to test if AI language models are good at helping with this task because it’s hard to get enough examples of people using the models in real-life situations. The authors created a new way to test these models, called Tapilot-Crossing, which has many different scenarios and is easy to use. They tested popular and advanced AI language models on this benchmark and found that they have challenges with interactive data analysis. The authors also came up with a new strategy called AIR that helps AI language models learn from their successes. |
Keywords
» Artificial intelligence » Large language model
