13274243a0
Keep K3 suite selection and report-schema scoring in bash, merge K3/vision dataset_args into dpv4 yamls, and pin EvalScope at 735d920ee911 with local patches. Co-authored-by: Cursor <cursoragent@cursor.com>
102 lines
3.6 KiB
Markdown
102 lines
3.6 KiB
Markdown
# 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](https://evalscope.readthedocs.io/en/latest/third_party/deepsearchqa.html) 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](https://modelscope.cn/datasets/google/deepsearchqa/summary) |
|
|
| **Paper** | [Paper](https://storage.googleapis.com/deepmind-media/DeepSearchQA/DeepSearchQA_benchmark_paper.pdf) |
|
|
| **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:**
|
|
```text
|
|
{question}
|
|
```
|
|
|
|
## Usage
|
|
|
|
### Using CLI
|
|
|
|
```bash
|
|
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
|
|
|
|
```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)
|
|
```
|