README.md

metadata

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 | 📄 Paper (Arxiv)

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:

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.

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.