| --- |
| license: cc-by-sa-4.0 |
| task_categories: |
| - table-question-answering |
| language: |
| - en |
| tags: |
| - finance |
| - 10k |
| - edgar |
| - agent |
| - benchmark |
| size_categories: |
| - 1M<n<10M |
| configs: |
| - config_name: column_documentation |
| data_files: |
| - split: train |
| path: data/column_documentation/column_documentation.parquet |
| - config_name: company_addresses |
| data_files: |
| - split: train |
| path: data/company_addresses/company_addresses.parquet |
| - config_name: column_comments |
| data_files: |
| - split: train |
| path: data/column_comments/column_comments.parquet |
| - config_name: sqlite_sequence |
| data_files: |
| - split: train |
| path: data/sqlite_sequence/sqlite_sequence.parquet |
| - config_name: table_documentation |
| data_files: |
| - split: train |
| path: data/table_documentation/table_documentation.parquet |
| - config_name: companies |
| data_files: |
| - split: train |
| path: data/companies/companies.parquet |
| - config_name: filings |
| data_files: |
| - split: train |
| path: data/filings/filings.parquet |
| - config_name: financial_facts |
| data_files: |
| - split: train |
| path: data/financial_facts/financial_facts.parquet |
| - config_name: company_tickers |
| data_files: |
| - split: train |
| path: data/company_tickers/company_tickers.parquet |
| - config_name: table_comments |
| data_files: |
| - split: train |
| path: data/table_comments/table_comments.parquet |
| --- |
| |
| # DDRBench: Deep Data Research Benchmark |
|
|
| [**📊 Leaderboard & Demo**](https://huggingface.co/spaces/thinkwee/DDR_Bench) | [**📄 Paper (Arxiv)**](https://arxiv.org/abs/2602.02039) |
|
|
| ## Overview |
| **DDRBench (Deep Data Research Benchmark)** is a comprehensive evaluation framework designed to assess the capabilities of Large Language Model (LLM) agents in performing complex, multi-turn data research and reasoning tasks. Unlike traditional Q&A benchmarks, DDRBench focuses on scenarios requiring deep interaction with structured databases, tool usage, and long-context reasoning. |
|
|
| This dataset repository specifically hosts the **10-K Financial Database**, a core component of the DDRBench suite. It contains structured financial data extracted from SEC 10-K filings, enabling agents to answer intricate financial questions that mimic real-world analyst workflows. |
|
|
| ## Dataset Structure |
| The dataset is organized into multiple configurations (subsets), representing different tables from the underlying SQLite database: |
|
|
| * **`financial_facts`**: The primary table containing over 5 million financial metrics (US-GAAP, IFRS) with values, units, and fiscal periods. |
| * **`companies`**: Registry of companies with CIK, names, and SIC codes. |
| * **`filings`**: Metadata for the SEC filings source documents. |
| * **`company_addresses`** & **`company_tickers`**: Geographic and market identification data. |
| * **`table_documentation`** & **`column_documentation`**: Meta-information describing the database schema to the agents. |
| |
| ## Usage |
| |
| ### Data Inspection |
| |
| Load specific tables using the `datasets` library: |
| |
| ```python |
| from datasets import load_dataset |
| |
| # Load the main financial facts table |
| financial_facts = load_dataset("thinkwee/DDRBench_10K", "financial_facts") |
| |
| # Load company information |
| companies = load_dataset("thinkwee/DDRBench_10K", "companies") |
| ``` |
| |
| For agent trajectories and evaluation logs, please refer to the [DDRBench Trajectory Dataset](https://huggingface.co/datasets/thinkwee/DDRBench_10K_trajectory). |
| |
| ### Run Deep Data Research |
| |
| Please use the database file under ``/raw`` path and refer to https://github.com/thinkwee/DDR_Bench for running the agent. |
| |
