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>
328 lines
13 KiB
Markdown
328 lines
13 KiB
Markdown
# ACEBench
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## 概述
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ACEBench 评估大语言模型在真实场景中使用工具的能力：选择正确的 API、填充参数、拒绝无法满足的请求，以及在模拟环境中驱动多步骤智能体任务。数据分为三类——`normal`（常规工具使用）、`special`（不完整、错误或超出范围的请求）和 `agent`（多步骤、多轮交互）——并在 17 个细粒度类别上进行报告。
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## 任务描述
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- **任务类型**：函数调用与智能体工具使用
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- **输入**：对话历史、API 规范，以及可选的时间或角色档案上下文
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- **输出**：一个 `[ApiName(key='value')]` 调用列表、一句诊断说明，或完整的智能体轨迹
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- **领域**：涵盖技术、金融、健康和社会等 8 个领域及 68 个子领域
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## 主要特性
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- 包含 1023 个英文样本和 1017 个中文样本，可通过 `extra_params.language` 选择。
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- 使用官方 ACEBench 提示词和官方 `[ApiName(...)]` 输出格式；若输出无法解析，则直接得零分，而非通过宽松解析进行挽救。
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- `normal_multi_turn_*` 类别按对话评分：只有所有步骤均正确，该对话才算正确，符合官方的逐轮聚合方式。
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- `agent` 类别会在 ACEBench 模拟的手机、外卖和旅行 API 上执行真实 rollout，并根据最终环境状态进行评分。
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## 评估说明
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- `accuracy` 是主要指标。对于 `normal` 和 `special` 类别，指答案准确率；对于 `agent` 类别，指最终状态准确率。`process_acc` 额外报告 `agent` 样本的关键里程碑进展，以及 `normal_multi_turn_*` 样本的逐轮进展。
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- 报告包含官方分组（ATOM、SINGLE_TURN、MULTI_TURN、NORMAL、SPECIAL、AGENT）和一个加权的 OVERALL 分数，权重为 `normal` 0.578 / `special` 0.2676 / `agent` 0.1545。权重会根据实际评估的分组重新归一化，因此部分运行结果仍具可解释性。
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- `agent_multi_turn` 还需要一个用户模拟器；请通过 `extra_params.user_model` 设置扮演用户的模型（官方运行器使用 `gpt-4o`）。若未设置，这些 rollout 将失败并得零分，因此在查看 OVERALL 分数前请务必配置此项。
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## 属性
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| 属性 | 值 |
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|----------|-------|
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| **基准测试名称** | `acebench` |
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| **数据集ID** | [evalscope/acebench](https://modelscope.cn/datasets/evalscope/acebench/summary) |
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| **论文** | N/A |
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| **标签** | `Agent`, `FunctionCalling`, `MultiTurn` |
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| **指标** | `accuracy`, `process_acc` |
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| **默认示例数** | 0-shot |
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| **评估划分** | `normal` |
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## 数据统计
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| 指标 | 值 |
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|--------|-------|
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| 总样本数 | 1,023 |
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| 提示词长度（平均） | 6032.98 字符 |
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| 提示词长度（最小/最大） | 2295 / 11835 字符 |
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**各子集统计数据：**
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| 子集 | 样本数 | 提示词平均长度 | 提示词最小长度 | 提示词最大长度 |
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|--------|---------|-------------|------------|------------|
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| `normal_single_turn_single_function` | 100 | 5165.79 | 2461 | 9553 |
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| `normal_single_turn_parallel_function` | 100 | 5036.21 | 2295 | 9644 |
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| `normal_multi_turn_user_adjust` | 123 | 4658.51 | 3172 | 6976 |
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| `normal_multi_turn_user_switch` | 100 | 7546.46 | 3467 | 11835 |
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| `normal_similar_api` | 50 | 3511.84 | 2484 | 6209 |
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| `normal_preference` | 50 | 8637.66 | 7107 | 10381 |
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| `normal_atom_bool` | 50 | 7377.62 | 4762 | 9727 |
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| `normal_atom_enum` | 50 | 7676.94 | 4927 | 11337 |
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| `normal_atom_number` | 50 | 7481.46 | 4851 | 10278 |
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| `normal_atom_list` | 50 | 7524.06 | 4910 | 10514 |
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| `normal_atom_object_deep` | 50 | 6102.02 | 2873 | 9755 |
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| `normal_atom_object_short` | 50 | 5139.5 | 2343 | 8921 |
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| `special_incomplete` | 50 | 6177.34 | 3473 | 10806 |
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| `special_error_param` | 50 | 4499.78 | 3121 | 6090 |
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| `special_irrelevant` | 50 | 6011.94 | 3778 | 8492 |
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| `agent_multi_step` | 20 | 6407.9 | 6343 | 6472 |
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| `agent_multi_turn` | 30 | 6290.97 | 5505 | 6630 |
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## 样例示例
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**子集**: `normal_single_turn_single_function`
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```json
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{
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"input": [
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{
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"id": "9198db95",
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"content": "You are an AI assistant with the role name \"assistant.\" Based on the provided API specifications and conversation history from steps 1 to t, generate the API requests that the assistant should call in step t+1. The API requests should be outp ... [TRUNCATED 3788 chars] ... '}, 'effects': {'description': 'List of audio effects to apply.', 'type': 'array', 'items': {'type': 'string', 'enum': ['reverb', 'echo', 'distortion']}}}, 'required': ['frequency', 'gain']}}}, 'required': ['microphone', 'performanceTime']}}]"
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},
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{
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"id": "61cfd720",
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"content": "Conversation history 1..t:\nuser: I have been fascinated recently with total solar eclipses. I am planning my next travel and would like to know when the next total solar eclipse will be visible in Greece, specifically in Athens, over the next five years.\n"
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}
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],
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"target": "{\"ground_truth\": {\"NightSkyAnalysis_performEclipseAnalysis\": {\"dateRange\": {\"startDate\": \"2023-01-01\", \"endDate\": \"2028-01-01\"}, \"location\": {\"latitude\": 37.9838, \"longitude\": 23.7275}, \"eclipseType\": \"total\"}}, \"mile_stone\": []}",
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"id": 0,
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"group_id": 0,
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"subset_key": "normal_single_turn_single_function",
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"metadata": {
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"id": "normal_single_turn_single_function_0",
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"test_category": "normal_single_turn_single_function",
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"dialogue_id": "normal_single_turn_single_function_0",
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"language": "en",
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"functions": [
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{
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"name": "NightSkyAnalysis_performEclipseAnalysis",
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"description": "Analyzes the occurrence of solar eclipses, categorizes them into types, and predicts future occurrences based on historical data and celestial mechanics.",
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"parameters": {
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"type": "object",
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"properties": {
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"dateRange": {
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"description": "The range of dates for which to analyze solar eclipses.",
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"type": "object",
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"properties": {
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"startDate": {
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"description": "The starting date for the analysis in YYYY-MM-DD format.",
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"type": "string"
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},
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"endDate": {
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"description": "The ending date for the analysis in YYYY-MM-DD format.",
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"type": "string"
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}
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},
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"required": [
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"startDate",
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"endDate"
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]
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},
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"location": {
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"description": "Geographical coordinates to focus the eclipse analysis.",
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"type": "object",
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"properties": {
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"latitude": {
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"description": "Latitude of the location.",
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"type": "number",
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"minimum": -90,
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"maximum": 90
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},
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"longitude": {
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"description": "Longitude of the location.",
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"type": "number",
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"minimum": -180,
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"maximum": 180
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}
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},
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"required": [
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"latitude",
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"longitude"
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]
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},
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"eclipseType": {
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"description": "The type of solar eclipse to specifically analyze.",
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"type": "string",
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"enum": [
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"total",
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"annular",
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"partial"
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]
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}
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},
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"required": [
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"dateRange",
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"location"
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]
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}
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},
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{
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"name": "AudioPerformanceOptimizer_optimizeMicrophoneSettings",
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"description": "Optimizes microphone settings for live performances, focusing on dynamic microphones to enhance sound quality and reduce feedback.",
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"parameters": {
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"type": "object",
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"properties": {
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"microphone": {
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"description": "Details of the microphone used.",
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"type": "object",
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"properties": {
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"type": {
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"description": "Type of the microphone.",
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"type": "string",
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"enum": [
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"dynamic",
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"condenser",
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"ribbon"
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]
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},
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"model": {
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"description": "Model of the microphone.",
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"type": "string"
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}
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},
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"required": [
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"type",
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"model"
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]
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},
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"performanceTime": {
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"description": "Scheduled time for the performance.",
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"type": "string",
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"enum": [
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"morning",
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"afternoon",
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"evening",
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"night"
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]
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},
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"environment": {
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"description": "Environmental conditions of the performance area.",
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"type": "object",
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"properties": {
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"humidity": {
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"description": "Humidity level as a percentage.",
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"type": "integer",
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"minimum": 0,
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"maximum": 100
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},
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"temperature": {
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"description": "Temperature in Celsius.",
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"type": "integer"
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}
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}
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},
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"soundSettings": {
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"description": "Specific sound settings to apply.",
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"frequency": {
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"description": "Frequency adjustments in Hz.",
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"type": "integer"
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},
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"gain": {
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"description": "Gain adjustments in dB.",
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"type": "integer"
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},
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"effects": {
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"description": "List of audio effects to apply.",
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"type": "array",
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"items": {
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"type": "string",
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"enum": [
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"reverb",
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"echo",
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"distortion"
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]
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}
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}
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},
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"required": [
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"frequency",
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"gain"
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]
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}
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}
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},
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"required": [
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"microphone",
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"performanceTime"
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]
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}
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}
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],
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"ground_truth": {
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"NightSkyAnalysis_performEclipseAnalysis": {
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"dateRange": {
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"startDate": "2023-01-01",
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"endDate": "2028-01-01"
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},
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"location": {
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"latitude": 37.9838,
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"longitude": 23.7275
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},
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"eclipseType": "total"
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}
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},
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"mile_stone": [],
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"initial_config": {},
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"involved_classes": [],
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"question": "user: I have been fascinated recently with total solar eclipses. I am planning my next travel and would like to know when the next total solar eclipse will be visible in Greece, specifically in Athens, over the next five years.\n",
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"time": "The current time is January 01, 2023, Sunday",
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"profile": ""
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}
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}
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```
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*注：部分内容因展示需要已被截断。*
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## 提示模板
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*未定义提示模板。*
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## 额外参数
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| 参数 | 类型 | 默认值 | 描述 |
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|-----------|------|---------|-------------|
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| `language` | `str` | `en` | 要评估的数据集语言，可选 `en` 或 `zh`。 |
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| `user_model` | `str` | `` | 在 `agent_multi_turn` rollout 中扮演用户的模型，例如 `gpt-4o`。若未设置，这些 rollout 将失败并得分为零。 |
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| `user_model_api_url` | `str` | `` | `user_model` 的基础 URL，默认为 `MODELSCOPE_API_BASE`。 |
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| `user_model_api_key` | `str` | `` | `user_model` 的 API 密钥，默认为 `MODELSCOPE_SDK_TOKEN`。 |
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| `max_dialog_turns` | `int` | `40` | 智能体 rollout 的最大步数。 |
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## 使用方法
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### 使用 CLI
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets acebench \
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--limit 10 # 正式评估时请删除此行
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```
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### 使用 Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['acebench'],
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dataset_args={
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'acebench': {
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# subset_list: ['normal_single_turn_single_function', 'normal_single_turn_parallel_function', 'normal_multi_turn_user_adjust'] # 可选，评估特定子集
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# extra_params: {} # 使用默认额外参数
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}
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},
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limit=10, # 正式评估时请删除此行
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)
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run_task(task_cfg=task_cfg)
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```
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