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_score; 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_score, 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)