14 KiB
14 KiB
ACEBench
Overview
ACEBench is a tool-use benchmark for evaluating whether large language models can select APIs, fill arguments, handle abnormal requests, and complete realistic agent tasks.
Task Description
- Task Type: Function calling and agentic tool use
- Input: Conversation history, API specifications, optional time/profile context, and agent task context
- Output: Function calls or diagnostic text for special cases
- Subsets: normal, special, and agent
Evaluation Notes
- The adapter passes ACEBench API specifications as EvalScope tools and also includes concise text instructions for text-only models.
- Normal samples are scored by matching function names and arguments.
- Special samples are scored against ACEBench's diagnostic text contract.
- Agent samples report
process_accagainst ACEBench milestones. If a model returns a final-state JSON object,end_state_accis also reported and used asacc; otherwiseaccfollowsprocess_acc.
Properties
| Property | Value |
|---|---|
| Benchmark Name | acebench |
| Dataset ID | evalscope/acebench |
| Paper | N/A |
| Tags | Agent, FunctionCalling, MultiTurn |
| Metrics | acc, process_acc, end_state_acc |
| Default Shots | 0-shot |
| Evaluation Split | normal |
Data Statistics
| Metric | Value |
|---|---|
| Total Samples | 1,023 |
| Prompt Length (Mean) | 4676.74 chars |
| Prompt Length (Min/Max) | 1007 / 10555 chars |
Per-Subset Statistics:
| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
|---|---|---|---|---|
normal |
823 | 4859.68 | 1070 | 10555 |
special |
150 | 3449.69 | 1007 | 8692 |
agent |
50 | 5346.72 | 4454 | 5656 |
Sample Example
Subset: normal
{
"input": [
{
"id": "134dd42a",
"content": "You are evaluating ACEBench tool-use tasks.\n\nUse the available tool schemas when native function calling is supported.\n\nFor text-only output, return API calls as [ApiName(key1='value1', key2=2)]. Return only the call list and no extra explana ... [TRUNCATED 2553 chars] ... \"}, \"effects\": {\"description\": \"List of audio effects to apply.\", \"type\": \"array\", \"items\": {\"type\": \"string\", \"enum\": [\"reverb\", \"echo\", \"distortion\"]}}}, \"required\": [\"frequency\", \"gain\"]}}}, \"required\": [\"microphone\", \"performanceTime\"]}}]"
},
{
"id": "da9681d9",
"content": "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.",
"role": "user"
}
],
"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\": []}",
"id": 0,
"group_id": 0,
"tools": [
{
"name": "NightSkyAnalysis_performEclipseAnalysis",
"description": "Analyzes the occurrence of solar eclipses, categorizes them into types, and predicts future occurrences based on historical data and celestial mechanics.",
"parameters": {
"type": "object",
"properties": {
"dateRange": {
"type": "object",
"description": "The range of dates for which to analyze solar eclipses.",
"properties": {
"startDate": {
"type": "string",
"description": "The starting date for the analysis in YYYY-MM-DD format."
},
"endDate": {
"type": "string",
"description": "The ending date for the analysis in YYYY-MM-DD format."
}
},
"required": [
"startDate",
"endDate"
]
},
"location": {
"type": "object",
"description": "Geographical coordinates to focus the eclipse analysis.",
"properties": {
"latitude": {
"type": "number",
"description": "Latitude of the location."
},
"longitude": {
"type": "number",
"description": "Longitude of the location."
}
},
"required": [
"latitude",
"longitude"
]
},
"eclipseType": {
"type": "string",
"description": "The type of solar eclipse to specifically analyze.",
"enum": [
"total",
"annular",
"partial"
]
}
},
"required": [
"dateRange",
"location"
]
}
},
{
"name": "AudioPerformanceOptimizer_optimizeMicrophoneSettings",
"description": "Optimizes microphone settings for live performances, focusing on dynamic microphones to enhance sound quality and reduce feedback.",
"parameters": {
"type": "object",
"properties": {
"microphone": {
"type": "object",
"description": "Details of the microphone used.",
"properties": {
"type": {
"type": "string",
"description": "Type of the microphone.",
"enum": [
"dynamic",
"condenser",
"ribbon"
]
},
"model": {
"type": "string",
"description": "Model of the microphone."
}
},
"required": [
"type",
"model"
]
},
"performanceTime": {
"type": "string",
"description": "Scheduled time for the performance.",
"enum": [
"morning",
"afternoon",
"evening",
"night"
]
},
"environment": {
"type": "object",
"description": "Environmental conditions of the performance area.",
"properties": {
"humidity": {
"type": "integer",
"description": "Humidity level as a percentage."
},
"temperature": {
"type": "integer",
"description": "Temperature in Celsius."
}
}
},
"soundSettings": {
"type": "array",
"description": "Specific sound settings to apply.",
"items": {
"type": "object",
"properties": {
"frequency": {
"type": "integer",
"description": "Frequency adjustments in Hz."
},
"gain": {
"type": "integer",
"description": "Gain adjustments in dB."
},
"effects": {
"type": "array",
"description": "List of audio effects to apply.",
"items": {
"type": "string",
"enum": [
"reverb",
"echo",
"distortion"
]
}
}
},
"required": [
"frequency",
"gain"
]
}
}
},
"required": [
"microphone",
"performanceTime"
]
}
}
],
"metadata": {
"id": "normal_single_turn_single_function_0",
"sub_category": "data_normal_single_turn_single_function",
"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",
"time": "",
"profile": "",
"functions": [
{
"name": "NightSkyAnalysis_performEclipseAnalysis",
"description": "Analyzes the occurrence of solar eclipses, categorizes them into types, and predicts future occurrences based on historical data and celestial mechanics.",
"parameters": {
"type": "object",
"properties": {
"dateRange": {
"description": "The range of dates for which to analyze solar eclipses.",
"type": "object",
"properties": {
"startDate": {
"description": "The starting date for the analysis in YYYY-MM-DD format.",
"type": "string"
},
"endDate": {
"description": "The ending date for the analysis in YYYY-MM-DD format.",
"type": "string"
}
},
"required": [
"startDate",
"endDate"
]
},
"location": {
"description": "Geographical coordinates to focus the eclipse analysis.",
"type": "object",
"properties": {
"latitude": {
"description": "Latitude of the location.",
"type": "number",
"minimum": -90,
"maximum": 90
},
"longitude": {
"description": "Longitude of the location.",
"type": "number",
"minimum": -180,
"maximum": 180
}
},
"required": [
"latitude",
"longitude"
]
},
"eclipseType": {
"description": "The type of solar eclipse to specifically analyze.",
"type": "string",
"enum": [
"total",
"annular",
"partial"
]
}
},
"required": [
"dateRange",
"location"
]
}
},
{
"name": "AudioPerformanceOptimizer_optimizeMicrophoneSettings",
"description": "Optimizes microphone settings for live performances, focusing on dynamic microphones to enhance sound quality and reduce feedback.",
"parameters": {
"type": "object",
"properties": {
"microphone": {
"description": "Details of the microphone used.",
"type": "object",
"properties": {
"type": {
"description": "Type of the microphone.",
"type": "string",
"enum": [
"dynamic",
"condenser",
"ribbon"
]
},
"model": {
"description": "Model of the microphone.",
"type": "string"
}
},
"required": [
"type",
"model"
]
},
"performanceTime": {
"description": "Scheduled time for the performance.",
"type": "string",
"enum": [
"morning",
"afternoon",
"evening",
"night"
]
},
"environment": {
"description": "Environmental conditions of the performance area.",
"type": "object",
"properties": {
"humidity": {
"description": "Humidity level as a percentage.",
"type": "integer",
"minimum": 0,
"maximum": 100
},
"temperature": {
"description": "Temperature in Celsius.",
"type": "integer"
}
}
},
"soundSettings": {
"description": "Specific sound settings to apply.",
"type": "array",
"items": {
"type": "object",
"properties": {
"frequency": {
"description": "Frequency adjustments in Hz.",
"type": "integer"
},
"gain": {
"description": "Gain adjustments in dB.",
"type": "integer"
},
"effects": {
"description": "List of audio effects to apply.",
"type": "array",
"items": {
"type": "string",
"enum": [
"reverb",
"echo",
"distortion"
]
}
}
},
"required": [
"frequency",
"gain"
]
}
}
},
"required": [
"microphone",
"performanceTime"
]
}
}
],
"ground_truth": {
"NightSkyAnalysis_performEclipseAnalysis": {
"dateRange": {
"startDate": "2023-01-01",
"endDate": "2028-01-01"
},
"location": {
"latitude": 37.9838,
"longitude": 23.7275
},
"eclipseType": "total"
}
},
"mile_stone": [],
"initial_config": {},
"involved_classes": []
}
}
Prompt Template
No prompt template defined.
Usage
Using CLI
evalscope eval \
--model YOUR_MODEL \
--api-url OPENAI_API_COMPAT_URL \
--api-key EMPTY_TOKEN \
--datasets acebench \
--limit 10 # Remove this line for formal evaluation
Using 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=['acebench'],
dataset_args={
'acebench': {
# subset_list: ['normal', 'special', 'agent'] # optional, evaluate specific subsets
}
},
limit=10, # Remove this line for formal evaluation
)
run_task(task_cfg=task_cfg)