evalstone/evalscope/docs/en/benchmarks/officeqa.md

OfficeQA

Overview

OfficeQA is a grounded reasoning benchmark by Databricks, built for evaluating model/agent performance on end-to-end grounded reasoning tasks over U.S. Treasury Bulletin documents (1939-2025).

Task Description

• Task Type: Agent-based Document QA (grep/search over corpus)
• Input: A question + access to parsed Treasury Bulletin text files via bash tools
• Output: A precise answer (numeric values, text, or structured data)
• Evaluation Mode: Agent with bash tool (grep, cat, etc.) over the corpus

Key Features

• Two subsets: officeqa_pro (133 questions, hard, default) and officeqa_full (246 questions, easy+hard)
• Corpus: ~900 parsed Treasury Bulletin text files (~460MB total)
• Agent uses bash tools (grep, cat, head, etc.) to search the corpus
• Scoring uses fuzzy numeric matching with configurable tolerance (1% default)

Evaluation Notes

• The agent is given access to parsed .txt files in a corpus directory
• Each question's source_files field indicates which document(s) contain the answer
• Uses rule-based scoring adapted from official reward.py
• Numerical answers matched with 1% relative error tolerance
• Text answers use case-insensitive substring matching

Properties

Property	Value
Benchmark Name	officeqa
Dataset ID	evalscope/officeqa
Paper	N/A
Tags	Agent, Knowledge, QA
Metrics	accuracy
Default Shots	0-shot
Evaluation Split	train

Data Statistics

Metric	Value
Total Samples	133
Prompt Length (Mean)	443.06 chars
Prompt Length (Min/Max)	165 / 1186 chars

Sample Example

Subset: default

{
  "input": [
    {
      "id": "a6357de6",
      "content": "What were the total expenditures (in millions of nominal dollars) for U.S national defense in the calendar year of 1940?\nPlease provide a precise and concise answer."
    }
  ],
  "target": "2,602",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "uid": "UID0001",
    "source_files": "treasury_bulletin_1941_01.txt",
    "difficulty": "hard"
  }
}

Prompt Template

Prompt Template:

{question}

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets officeqa \
    --agent-config '{"mode":"native","strategy":"function_calling","max_steps":15}' \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import TaskConfig, run_task
from evalscope.api.agent import NativeAgentConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['officeqa'],
    agent_config=NativeAgentConfig(
        strategy='function_calling',
        max_steps=15,
    ),
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)