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

DeepSearchQA

Overview

DeepSearchQA is a Google DeepMind benchmark for evaluating deep research agents on difficult multi-step information-seeking tasks across the open web. It contains 900 prompts spanning 17 domains and is designed to measure exhaustive answer-set generation rather than single-answer retrieval alone.

Task Description

• Task Type: Search-agent factual question answering
• Input: A natural-language research question
• Output: A single answer or complete answer set, depending on the question
• Grading: LLM-as-judge semantic matching against the gold answer and answer type

Key Features

• Tests systematic collation of fragmented information from multiple sources
• Requires entity resolution and de-duplication for set-answer tasks
• Penalizes both under-retrieval and excessive/hallucinated answers
• Uses problem_category for analysis metadata; answer_type is withheld from the model during inference
• Compatible with EvalScope agent configurations for native or external web-capable agents

Agent Tool Configuration

DeepSearchQA does not hard-code a search provider. By default it runs through EvalScope native AgentLoop without external search tools. To evaluate a web-capable agent, set TaskConfig.agent_config and attach the search/fetch tools that should be available to the model. If NativeAgentConfig.max_steps is omitted, DeepSearchQA uses its benchmark-level AgentLoop default of 30 steps.

See the DeepSearchQA usage guide for runtime examples, MCP search/fetch configuration, and evaluation notes.

Evaluation Notes

• EvalScope loads the ModelScope dataset google/deepsearchqa from the eval split.
• LLM judge is enabled by default. Official starter code uses Gemini 2.5 Flash with the DeepSearchQA judge prompt, but EvalScope can use any configured judge model for local runs.
• The primary metric is f1; precision, recall, and empty/invalid response rates are also reported.
• JudgeStrategy.RULE provides a conservative exact/substring fallback for smoke tests and is not equivalent to official LLM judging.

Properties

Property	Value
Benchmark Name	deepsearchqa
Dataset ID	google/deepsearchqa
Paper	Paper
Tags	Agent, Knowledge, QA, Retrieval
Metrics	f1, precision, recall
Default Shots	0-shot
Evaluation Split	eval

Data Statistics

Metric	Value
Total Samples	900
Prompt Length (Mean)	295.54 chars
Prompt Length (Min/Max)	49 / 1007 chars

Sample Example

Sample example not available.

Prompt Template

Prompt Template:

{question}

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets deepsearchqa \
    --agent-config '{"mode":"native","strategy":"function_calling","max_steps":30}' \
    --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=['deepsearchqa'],
    agent_config=NativeAgentConfig(
        strategy='function_calling',
        max_steps=30,
    ),
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)