8.2 KiB
8.2 KiB
GDPval
概述
GDPval 用于评估模型是否能够完成具有现实经济价值的工作任务，并生成所要求的交付文件。该适配器针对 OpenAI 公开的 220 个任务的 gold 子集，该子集在 ModelScope 上镜像为 openai-mirror/gdpval。
任务描述
- 任务类型：代理式专业工作 / 交付物生成
- 输入：职场风格的任务提示，可选附带参考文件
- 输出：最终回复文本及位于
deliverable_files/目录下的请求文件 - 数据集：OpenAI 公开的 GDPval gold 子集，包含 220 个任务
主要特性
- 使用原生 EvalScope 的
AgentLoopAdapter，支持 bash 和 Python 执行工具。 - 默认从 ModelScope 加载记录和参考文件。
- 将选定的参考文件以只读方式挂载到沙箱中的
/reference_files目录下。 - 在沙箱销毁前提取写入
deliverable_files/的文件。 - 生成符合 GDPval 格式的提交包，包含
deliverable_text和deliverable_files列。
评估说明
- 默认 Docker 镜像为
evalscope/gdpval:latest，若本地缺失则会根据内置的 Dockerfile 自动构建。设置extra_params.auto_build_docker_image=false可强制使用预构建镜像，或通过extra_params.docker_image覆盖默认镜像。 submission_ready是一个本地就绪指标：当模型生成了最终文本或至少一个交付文件时，其值为 1。该指标并非官方 GDPval 质量评分。- EvalScope 不运行本地 GDPval 评判器。请使用导出的提交包配合 OpenAI 官方 GDPval 评判器获取质量评分。
- 文档/电子表格/幻灯片等完整文件的质量依赖于 GDPval 运行时镜像。轻量级 Python 镜像仅适用于基础流程的冒烟测试。
评分与提交
- EvalScope 会在报告目录下写入一个本地提交文件夹。
- 提交内容包含符合 GDPval 数据集格式的
deliverable_text和deliverable_files字段。 - 官方 GDPval 评分需在外部进行。请对导出的提交包运行 OpenAI 官方 GDPval 评判器。
属性
| 属性 | 值 |
|---|---|
| 基准测试名称 | gdpval |
| 数据集ID | openai-mirror/gdpval |
| 论文 | N/A |
| 标签 | Agent, Knowledge, MultiTurn |
| 指标 | submission_ready |
| 默认示例数 | 0-shot |
| 评估分割 | train |
数据统计
| 指标 | 值 |
|---|---|
| 总样本数 | 220 |
| 提示词长度（平均） | 2742.59 字符 |
| 提示词长度（最小/最大） | 1058 / 7160 字符 |
样例示例
子集: default
{
"input": [
{
"id": "3315415d",
"content": "You are an auditor and as part of an audit engagement, you are tasked with reviewing and testing the accuracy of reported Anti-Financial Crime Risk Metrics.\n\nThe attached spreadsheet titled ‘Population’ contains Anti-Financial Crime Risk Metr ... [TRUNCATED 2069 chars] ... folder named `deliverable_files` in the sandbox working directory.\nWe will grade your final message as part of the deliverable, but requested documents, spreadsheets, slides, media,\nor archives should be actual files in `deliverable_files`.\n"
}
],
"target": "",
"id": 0,
"group_id": 0,
"tools": [
{
"name": "bash",
"description": "Execute a bash command inside the sandbox environment. Returns the combined stdout / stderr output of the command.",
"parameters": {
"properties": {
"command": {
"type": "string",
"description": "The bash command to execute."
},
"timeout": {
"type": "number",
"description": "Maximum execution time in seconds (default: 60).",
"default": 60
}
},
"required": [
"command"
]
}
},
{
"name": "python_exec",
"description": "Execute Python source code inside the sandbox environment. Returns stdout and stderr output.",
"parameters": {
"properties": {
"code": {
"type": "string",
"description": "Python source code to execute."
},
"timeout": {
"type": "number",
"description": "Maximum execution time in seconds (default: 60).",
"default": 60
}
},
"required": [
"code"
]
}
}
],
"metadata": {
"task_id": "83d10b06-26d1-4636-a32c-23f92c57f30b",
"sector": "Professional, Scientific, and Technical Services",
"occupation": "Accountants and Auditors",
"prompt": "You are an auditor and as part of an audit engagement, you are tasked with reviewing and testing the accuracy of reported Anti-Financial Crime Risk Metrics.\n\nThe attached spreadsheet titled ‘Population’ contains Anti-Financial Crime Risk Metr ... [TRUNCATED 1526 chars] ... across all Divisions and sub-Divisions.\n\n4. Create a new spreadsheet titled ‘Sample’:\n- Tab 1: Selected sample, copied from the original ‘Population’ sheet, with selected rows marked in column K.\n- Tab 2: Workings for sample size calculation.",
"reference_files": [
"reference_files/cc781e4dc0985c8eb327a53ec03b5900/Population v2.xlsx"
],
"reference_file_urls": [
"https://huggingface.co/datasets/openai/gdpval/resolve/main/reference_files/cc781e4dc0985c8eb327a53ec03b5900/Population%20v2.xlsx"
],
"reference_file_hf_uris": [
"hf://datasets/openai/gdpval@main/reference_files/cc781e4dc0985c8eb327a53ec03b5900/Population%20v2.xlsx"
],
"reference_paths": [
"reference_files/Population v2.xlsx"
],
"sandbox_reference_paths": [
"/reference_files/cc781e4dc0985c8eb327a53ec03b5900/Population v2.xlsx"
],
"rubric_pretty": "[+2] The submitted deliverable is an Excel workbook file whose basename is 'Sample' (accept .xlsx, .xls, or .xlsm).\n\n[+2] The workbook contains a worksheet named exactly 'Sample Size Calculation' (case-insensitive, ignoring surrounding spaces ... [TRUNCATED 4861 chars] ... ntage changes (e.g., |J| ≥ 100%) are made easily identifiable (such as by a separate flag, note, or conditional formatting).\n\n[+1] The first worksheet is named 'Sample' (case-insensitive).\n\n[+5] Overall formatting and style of the deliverable",
"rubric_json": "[{\"score\": 2, \"criterion\": \"The submitted deliverable is an Excel workbook file whose basename is 'Sample' (accept .xlsx, .xls, or .xlsm).\", \"required\": null, \"rubric_item_id\": \"1d43f1eb-4011-47ac-8ad7-a3c467639a6a\", \"author_type\": \"human\", \" ... [TRUNCATED 11817 chars] ... ull}, {\"score\": 5, \"criterion\": \"Overall formatting and style of the deliverable\", \"required\": null, \"rubric_item_id\": \"a64588ed-db04-4b8b-b3b8-3674ddcf10d1\", \"author_type\": \"human\", \"tags\": [\"true\"], \"read_only\": null, \"form_content\": null}]",
"dataset_id": "openai-mirror/gdpval",
"dataset_hub": "modelscope"
}
}
提示模板
提示模板:
{question}
额外参数
| 参数 | 类型 | 默认值 | 描述 |
|---|---|---|---|
max_steps |
int |
250 |
每个样本的最大代理步骤数。 |
command_timeout |
float |
180.0 |
每条命令的默认超时时间（秒）。 |
docker_image |
str |
evalscope/gdpval:latest |
用作每个样本沙箱的 Docker 镜像。 |
auto_build_docker_image |
bool |
True |
若本地缺失默认 GDPval Docker 镜像，则自动构建。 |
network_enabled |
bool |
True |
允许沙箱访问网络。 |
download_reference_files |
bool |
True |
在推理前从数据集中心下载每个选定样本的参考文件。 |
使用方法
使用 CLI
evalscope eval \
--model YOUR_MODEL \
--api-url OPENAI_API_COMPAT_URL \
--api-key EMPTY_TOKEN \
--datasets gdpval \
--limit 10 # 正式评估时请删除此行
使用 Python
from evalscope import run_task
from evalscope.config import TaskConfig
task_cfg = TaskConfig(
model='YOUR_MODEL',
api_url='OPENAI_API_COMPAT_URL',
api_key='EMPTY_TOKEN',
datasets=['gdpval'],
dataset_args={
'gdpval': {
# extra_params: {} # 使用默认额外参数
}
},
limit=10, # 正式评估时请删除此行
)
run_task(task_cfg=task_cfg)