InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
This work addresses the need for standardized evaluation of AI agents in data analysis, though it is incremental as it builds on existing LLM and benchmarking methods.
The authors introduced InfiAgent-DABench, a benchmark for evaluating LLM-based agents on data analysis tasks, including a dataset of 257 questions and an agent framework, and found that their specialized DAAgent outperformed GPT-3.5 by 3.9% on the benchmark.
In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks. These tasks require agents to end-to-end solving complex tasks by interacting with an execution environment. This benchmark contains DAEval, a dataset consisting of 257 data analysis questions derived from 52 CSV files, and an agent framework which incorporates LLMs to serve as data analysis agents for both serving and evaluation. Since data analysis questions are often open-ended and hard to evaluate without human supervision, we adopt a format-prompting technique to convert each question into a closed-form format so that they can be automatically evaluated. Our extensive benchmarking of 34 LLMs uncovers the current challenges encountered in data analysis tasks. In addition, building on top of our agent framework, we develop a specialized agent, DAAgent, which surpasses GPT-3.5 by 3.9% on DABench. Evaluation datasets and toolkits for InfiAgent-DABench are released at https://github.com/InfiAgent/InfiAgent .