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--- |
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license: cc-by-sa-4.0 |
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task_categories: |
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- table-question-answering |
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language: |
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- en |
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tags: |
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- finance |
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- 10k |
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- edgar |
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- agent |
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- benchmark |
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size_categories: |
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- 1M<n<10M |
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configs: |
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- config_name: column_documentation |
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data_files: |
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- split: train |
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path: data/column_documentation/column_documentation.parquet |
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- config_name: company_addresses |
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data_files: |
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- split: train |
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path: data/company_addresses/company_addresses.parquet |
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- config_name: column_comments |
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data_files: |
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- split: train |
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path: data/column_comments/column_comments.parquet |
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- config_name: sqlite_sequence |
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data_files: |
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- split: train |
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path: data/sqlite_sequence/sqlite_sequence.parquet |
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- config_name: table_documentation |
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data_files: |
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- split: train |
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path: data/table_documentation/table_documentation.parquet |
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- config_name: companies |
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data_files: |
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- split: train |
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path: data/companies/companies.parquet |
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- config_name: filings |
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data_files: |
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- split: train |
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path: data/filings/filings.parquet |
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- config_name: financial_facts |
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data_files: |
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- split: train |
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path: data/financial_facts/financial_facts.parquet |
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- config_name: company_tickers |
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data_files: |
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- split: train |
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path: data/company_tickers/company_tickers.parquet |
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- config_name: table_comments |
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data_files: |
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- split: train |
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path: data/table_comments/table_comments.parquet |
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--- |
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# DDRBench: Deep Data Research Benchmark |
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[**📊 Leaderboard & Demo**](https://huggingface.co/spaces/thinkwee/DDR_Bench) | [**📄 Paper (Arxiv)**](https://arxiv.org/abs/2602.02039) |
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## Overview |
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**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. |
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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. |
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## Dataset Structure |
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The dataset is organized into multiple configurations (subsets), representing different tables from the underlying SQLite database: |
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* **`financial_facts`**: The primary table containing over 5 million financial metrics (US-GAAP, IFRS) with values, units, and fiscal periods. |
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* **`companies`**: Registry of companies with CIK, names, and SIC codes. |
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* **`filings`**: Metadata for the SEC filings source documents. |
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* **`company_addresses`** & **`company_tickers`**: Geographic and market identification data. |
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* **`table_documentation`** & **`column_documentation`**: Meta-information describing the database schema to the agents. |
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## Usage |
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### Data Inspection |
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Load specific tables using the `datasets` library: |
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```python |
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from datasets import load_dataset |
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# Load the main financial facts table |
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financial_facts = load_dataset("thinkwee/DDRBench_10K", "financial_facts") |
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# Load company information |
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companies = load_dataset("thinkwee/DDRBench_10K", "companies") |
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``` |
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For agent trajectories and evaluation logs, please refer to the [DDRBench Trajectory Dataset](https://huggingface.co/datasets/thinkwee/DDRBench_10K_trajectory). |
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### Run Deep Data Research |
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Please use the database file under ``/raw`` path and refer to https://github.com/thinkwee/DDR_Bench for running the agent. |
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