InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
Belangrijkste concepten
LLM-based agents face challenges in data analysis tasks, leading to the development of InfiAgent-DABench for evaluation.
Samenvatting
1. Introduction
Introduction of InfiAgent-DABench, a benchmark for LLM-based agents.
Large language model-based agents are popular in AI society.
Data analysis tasks are challenging but useful for LLM-based agents.
2. InfiAgent-DABench Benchmark
DAEval dataset and agent framework designed for evaluating LLMs on data analysis tasks.
Dataset construction involves real-world CSV files and closed-form questions.
Human assessment ensures dataset quality.
3. Experiments
Models categorized into proprietary, open-source general LLMs, open-source code LLMs, and agent frameworks.
Implementation details include reformatting responses to match format requirements.
4. Results
Performance comparison of benchmarked models on the validation set of DAEval.
Key findings include challenges faced by current LLMs in data analysis tasks.
5. Conclusion
Introduction of InfiAgent-DABench as a valuable benchmark for assessing LLM-based agents in data analysis tasks.
Development of DAAgent specialized for data analysis with improved performance over GPT-3.5.
InfiAgent-DABench
Statistieken
Life Expectancy: 0.94143
Country: Switzerland
Happiness Rank: 1
GDP per Capita: 1.39651
Happiness_rank.csv:
Is there a linear relationship between the GDP per capita and the life expectancy score in the Happiness_rank.csv? Conduct linear regression and use the resulting coefficient of determination (R-squared) to evaluate the model's goodness of fit...
The R-squared value is approximately 0.67, which indicates a poor fit for the linear regression model.
Citaten
"Our extensive benchmarking of 34 cutting-edge LLMs reveals that contemporary models still face challenges in effectively managing data analysis tasks."
"DAAgent achieves a better performance with GPT-3.5 by 3.89%, although it has much less parameters than that proprietary model."