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Researchers Say New AI Outperforms Other Models on Data Science Tasks
by Huge Data Tech. Our Data's Bigger than Yours :-)January 2nd, 2025
Too Long; Didn't Read
Researchers have developed a solution that emphasizes three pivotal techniques to augment problem-solving in data science.
Authors:
(1) Sirui Hong, DeepWisdom and these authors contributed equally to this work;
(2) Yizhang Lin, DeepWisdom and these authors contributed equally to this work;
(3) Bang Liu, Universite de Montreal & Mila and these author are listed in alphabetical order;
(4) Bangbang Liu, DeepWisdom and these authors contributed equally to this work;
(5) Binhao Wu, DeepWisdom and these authors contributed equally to this work;
(6) Danyang Li, DeepWisdom and these authors contributed equally to this work;
(7) Jiaqi Chen, Fudan University and these authors contributed equally to this work;
(8) Jiayi Zhang, Renmin University of China and these authors contributed equally to this work;
(9) Jinlin Wang, DeepWisdom and these authors contributed equally to this work;
(10) Li Zhang, Fudan University and these authors contributed equally to this work;
(11) Lingyao Zhang, these authors contributed equally to this work;
(12) Min Yang, 5Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences and these authors contributed equally to this work;
(13) Mingchen Zhuge, AI Initiative, King Abdullah University of Science and Technology and these authors contributed equally to this work;
(14) Taicheng Guo, University of Notre Dame and these authors contributed equally to this work;
(15) Tuo Zhou, The University of Hong Kong and these authors contributed equally to this work;
(16) Wei Tao, Fudan University and these authors contributed equally to this work;
(17) Wenyi Wang, AI Initiative, King Abdullah University of Science and Technology and these authors contributed equally to this work;
(18) Xiangru Tang, Yale University and these authors contributed equally to this work;
(19) Xiangtao Lu, DeepWisdom and these authors contributed equally to this work;
(20) Xiawu Zheng, Xiamen University and these authors contributed equally to this work;
(21) Xinbing Liang, DeepWisdom, East China Normal University and these authors contributed equally to this work;
(22) Yaying Fei, Beijing University of Technology and these authors contributed equally to this work;
(23) Yuheng Cheng, The Chinese University of Hong Kong, Shenzhen and these authors contributed equally to this work;
(24) Zongze Xu, DeepWisdom, Hohai University and these authors contributed equally to this work;
(25) Chenglin Wu, DeepWisdom and a corresponding author.
Editor's Note: This is Part 5 of 5 of a research study detailing the development of Data Interpreter, a solution for various data science and real-world tasks. Read the rest below.
Table of Links
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3 Methodology and 3.1 Dynamic planning with Hierarchical Structure
- 3.2 Tool utilization and generation
- 3.3 Enhancing reasoning with verification and experience
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- 4.1 Experimental Setup
- 4.2 Main Result
- 4.3 Ablation Study
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A. Additional Results
B. Implementation Results
C. Details of Datasets
5 CONCLUSION
In this paper, we introduced the Data Interpreter, a solution for data science problem-solving through dynamic planning with hierarchical graphs, tools integration and evolution, and automated confidence-based verification. Our Data Interpreter is meticulously designed to address the data dependence intensity, refined domain knowledge, and rigorous logic requirements inherent in data science, enhancing reliability, automation, and reasoning capability in managing sophisticated data science tasks. Through extensive evaluations, our Data Interpreter has outperformed various opensource frameworks in machine learning tasks, mathematical problems, and real-world task performance, signifying a substantial advancement in the capabilities of LLM-based agents for data science.
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A ADDITIONAL RESULTS
A.1 ADDITIONAL RESULTS OF ML-BENCHMARK AND MATH DATASET
For a deeper understanding, Table 5 presents the results on the ML-benchmark for both Completion Rate and Normalized Performance Score metrics. Additionally, Table 6 showcases the results of ablation experiments on the ML-benchmark, focusing on the completion rate (CR) and normalized performance score (NPS).
A.2 ADDITIONAL RESULTS OF OPEN-ENDED TASKS
We present the results by the Data Interpreter of several open-ended tasks in two figures: tasks 8, 9, 10, and 13 in Figure 9, and tasks 4, 14, and 15 in Figure 10.
A.3 RESULT OF DATA VISUALIZATION
Figure 11 illustrates the results of data analysis and visualization of the Data Interpreter.
B IMPLEMENTATION DETAILS
B.1 AN EXAMPLE OF PLAN
Here is an example of a plan. The user requirement is: “This is a dataset featuring sensor readings from industrial machines, aimed at predicting machine operational status (normal or faulty). Visualize the analysis and prediction results with high-quality graphs. Train data path: {train path}, eval data path: {eval path}.”
B.2 NODE COMPONENTS IN THE TASK GRAPH
Figure 12 illustrates the structure of each task. Each task is structured to include instructions, dependencies array, code, and a flag. Specifically, dependencies array and flag are designed to maintain and manage the node’s dependency and runtime status, while instructions and code describe tasks in natural and coding languages respectively.
Since the code of each task is automatically executed in the executor, the corresponding code is directly entered into the executor used by its predecessor task to ensure the consistency of code variables between sequential tasks. During the task execution process, the execution results will be stored as runtime results.
B.3 TOOLS DETAILS
The tools of our Data Interpreter are listed in Table 7
B.3.1 AN EXAMPLE OF TOOL SCHEMA
B.3.2 TOOL USAGE PROMPTS
We use two types of prompts for tool utilization. For open-ended tasks, we use zero-shot prompts, and for machine-learning tasks, we use one-shot prompts as illustrated below.
C DETAILS OF DATASETS
C.1 OPEN-ENDED TASK DETAILS
Figures 13 to 16 showcase several typical open-ended tasks in the following illustrations. For each task, we include the necessary data, user requirements, and assessment pipeline.
C.2 ML-BENCHMARK DATASET DESCRIPTION
Here are the details about the ML-Benchmark dataset. We collect several typical datasets from Kaggle[1] and machine learning. Details are in Table 8
This paper is available on arxiv under CC BY 4.0 DEED license.
L O A D I N G
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