Datasets:

MichaelYang-lyx
/

AIDA

@@ -1,118 +1,80 @@
-language:- zh
 license: apache-2.0
 task_categories:
 - question-answering
 - tabular-classification
 - text-generation
 tags:
 - data-analytics
 - agents
 - document-understanding
 - benchmark
 pretty_name: AIDABench
----# Dataset Card for AIDABench## Dataset Summary
 [cite_start]As AI-driven document understanding and processing tools become increasingly prevalent, the need for rigorous evaluation standards has grown[cite: 7]. [cite_start]**AIDABench** is a comprehensive benchmark designed for evaluating AI systems on complex Data Analytics tasks in an end-to-end manner[cite: 9].
-[cite_start]It encompasses over 600 diverse document analytical tasks grounded in realistic scenarios, involving heterogeneous data types such as spreadsheets, databases, financial reports, and operational records[cite: 10, 11]. [cite_start]The tasks are highly challenging; even human experts require 1-2 hours per question when assisted by AI tools[cite: 12].*(💡 建议在这里插入论文的 Figure 1，展示整体框架结构。)*
 ![Overview of AIDABench Framework](./images/figure1_overview.png)
 [cite_start]*Figure 1: Overview of the AIDABench evaluation framework[cite: 65].*
 ## Dataset Structure
 ### Task Categories
-[cite_start]The dataset comprises three primary capability dimensions covering the end-to-end document processing pipeline[cite: 67, 199]:* [cite_start]**File Generation (43.3%):** Assesses data wrangling operations like filtering, format normalization, deduplication, and cross-sheet linkage[cite: 72, 199].
-* [cite_start]**Question Answering (QA) (37.5%):** Evaluates analytical operations including summation, mean computation, ranking, and trend analysis[cite: 71, 199].* [cite_start]**Data Visualization (19.2%):** Measures the ability to generate and adapt multiple visualization forms (bar, line, pie charts) and style customizations[cite: 73, 199].
 *(💡 建议在这里插入论文的 Figure 2，直观展示三种任务的输入输出示例。)*
 ![Evaluation Scenarios](./images/figure2_scenarios.png)
-[cite_start]*Figure 2: Example evaluation scenarios for QA, Data Visualization, and File Generation[cite: 187, 188, 190].*### Task Complexity
-[cite_start]Tasks are stratified by the number of expert-level reasoning steps required[cite: 201]:* [cite_start]**Easy (29.5%):** $\le$ 3 steps[cite: 202].
-* [cite_start]**Medium (49.4%):** 4-6 steps[cite: 202].* [cite_start]**Hard (21.1%):** > 7 steps[cite: 202].
-* [cite_start]**Cross-file Reasoning:** 27.4% of tasks require joint reasoning over multiple input files (up to 14 files)[cite: 205].### Data Formats
 [cite_start]Tabular files dominate the distribution (xlsx/csv account for 91.8%), complemented by DOCX and PDF formats to support mixed-type processing[cite: 200].
 ## Evaluation Framework
 [cite_start]All models are evaluated under a unified, tool-augmented protocol where the model receives task instructions and files, and can execute arbitrary Python code in a sandboxed environment[cite: 211, 212, 214].
-[cite_start]To align with the task categories, AIDABench utilizes three dedicated LLM-based evaluators[cite: 222]:1.  [cite_start]**QA Evaluator:** A binary judge powered by QwQ-32B[cite: 227, 326].
-2.  [cite_start]**Visualization Evaluator:** Powered by Gemini 3 Pro, scoring both correctness and readability[cite: 235, 326].3.  [cite_start]**Spreadsheet File Evaluator:** Powered by Claude Sonnet 4.5, utilizing a coarse-to-fine verification strategy[cite: 249, 326].
 *(💡 建议在这里插入论文的 Figure 3，解释自动化评测是怎么运作的。)*
 ![Evaluator Design](./images/figure3_evaluators.png)
-[cite_start]*Figure 3: The design of the three types of evaluators in AIDABench[cite: 313].*## Baseline Performance
 Results reveal that complex data analytics tasks remain a significant challenge. [cite_start]The best-performing model (Claude-Sonnet-4.5) achieved only a 59.43 pass@1 score[cite: 14, 349].
 ## Citation
 If you use this dataset, please cite the original paper:
 ```bibtex
 @article{yang2026aidabench,
   title={AIDABENCH: AI DATA ANALYTICS BENCHMARK},
   author={Yang, Yibo and Lei, Fei and Sun, Yixuan and others},
   journal={arXiv preprint},
   year={2026}
 }
-Github Repository: https://github.com/MichaelYang-lyx/AIDABench

+---
+language:
+- en
 license: apache-2.0
 task_categories:
 - question-answering
 - tabular-classification
 - text-generation
 tags:
 - data-analytics
 - agents
 - document-understanding
 - benchmark
 pretty_name: AIDABench
+---
+# Dataset Card for AIDABench
+## Dataset Summary
 [cite_start]As AI-driven document understanding and processing tools become increasingly prevalent, the need for rigorous evaluation standards has grown[cite: 7]. [cite_start]**AIDABench** is a comprehensive benchmark designed for evaluating AI systems on complex Data Analytics tasks in an end-to-end manner[cite: 9].
+[cite_start]It encompasses over 600 diverse document analytical tasks grounded in realistic scenarios, involving heterogeneous data types such as spreadsheets, databases, financial reports, and operational records[cite: 10, 11]. [cite_start]The tasks are highly challenging; even human experts require 1-2 hours per question when assisted by AI tools[cite: 12].
+*(💡 建议在这里插入论文的 Figure 1，展示整体框架结构。)*
 ![Overview of AIDABench Framework](./images/figure1_overview.png)
 [cite_start]*Figure 1: Overview of the AIDABench evaluation framework[cite: 65].*
 ## Dataset Structure
 ### Task Categories
+[cite_start]The dataset comprises three primary capability dimensions covering the end-to-end document processing pipeline[cite: 67, 199]:
+* [cite_start]**File Generation (43.3%):** Assesses data wrangling operations like filtering, format normalization, deduplication, and cross-sheet linkage[cite: 72, 199].
+* [cite_start]**Question Answering (QA) (37.5%):** Evaluates analytical operations including summation, mean computation, ranking, and trend analysis[cite: 71, 199].
+* [cite_start]**Data Visualization (19.2%):** Measures the ability to generate and adapt multiple visualization forms (bar, line, pie charts) and style customizations[cite: 73, 199].
 *(💡 建议在这里插入论文的 Figure 2，直观展示三种任务的输入输出示例。)*
 ![Evaluation Scenarios](./images/figure2_scenarios.png)
+[cite_start]*Figure 2: Example evaluation scenarios for QA, Data Visualization, and File Generation[cite: 187, 188, 190].*
+### Task Complexity
+[cite_start]Tasks are stratified by the number of expert-level reasoning steps required[cite: 201]:
+* [cite_start]**Easy (29.5%):** $\le$ 3 steps[cite: 202].
+* [cite_start]**Medium (49.4%):** 4-6 steps[cite: 202].
+* [cite_start]**Hard (21.1%):** > 7 steps[cite: 202].
+* [cite_start]**Cross-file Reasoning:** 27.4% of tasks require joint reasoning over multiple input files (up to 14 files)[cite: 205].
+### Data Formats
 [cite_start]Tabular files dominate the distribution (xlsx/csv account for 91.8%), complemented by DOCX and PDF formats to support mixed-type processing[cite: 200].
 ## Evaluation Framework
 [cite_start]All models are evaluated under a unified, tool-augmented protocol where the model receives task instructions and files, and can execute arbitrary Python code in a sandboxed environment[cite: 211, 212, 214].
+[cite_start]To align with the task categories, AIDABench utilizes three dedicated LLM-based evaluators[cite: 222]:
+1.  [cite_start]**QA Evaluator:** A binary judge powered by QwQ-32B[cite: 227, 326].
+2.  [cite_start]**Visualization Evaluator:** Powered by Gemini 3 Pro, scoring both correctness and readability[cite: 235, 326].
+3.  [cite_start]**Spreadsheet File Evaluator:** Powered by Claude Sonnet 4.5, utilizing a coarse-to-fine verification strategy[cite: 249, 326].
 *(💡 建议在这里插入论文的 Figure 3，解释自动化评测是怎么运作的。)*
 ![Evaluator Design](./images/figure3_evaluators.png)
+[cite_start]*Figure 3: The design of the three types of evaluators in AIDABench[cite: 313].*
+## Baseline Performance
 Results reveal that complex data analytics tasks remain a significant challenge. [cite_start]The best-performing model (Claude-Sonnet-4.5) achieved only a 59.43 pass@1 score[cite: 14, 349].
 ## Citation
 If you use this dataset, please cite the original paper:
 ```bibtex
 @article{yang2026aidabench,
   title={AIDABENCH: AI DATA ANALYTICS BENCHMARK},
   author={Yang, Yibo and Lei, Fei and Sun, Yixuan and others},
   journal={arXiv preprint},
   year={2026}
 }
+```
+Github Repository: https://github.com/MichaelYang-lyx/AIDABench
+---