General-VMCQ#
Overview#
General-VMCQ is a customizable visual multiple-choice question answering benchmark for multimodal models. It uses MMMU-style format with image placeholders in text, supporting flexible image inputs.
Task Description#
Task Type: Visual Multiple-Choice Question Answering
Input: Question with
<image N>placeholders + choice options + imagesOutput: Selected answer choice
Flexibility: Supports custom datasets via local files
Key Features#
MMMU-style format (not OpenAI message format)
Supports up to 100 images per sample
Flexible image input (path, URL, or base64 data URL)
Chain-of-thought prompt template option
Custom dataset support via local file loading
Evaluation Notes#
Default configuration uses 0-shot evaluation
Primary metric: Accuracy
Train split: dev, Eval split: val
Images are plain strings (do not wrap in
{{"url": ...}})See User Guide for dataset format
Properties#
Property |
Value |
|---|---|
Benchmark Name |
|
Dataset ID |
|
Paper |
N/A |
Tags |
|
Metrics |
|
Default Shots |
0-shot |
Evaluation Split |
|
Train Split |
|
Data Statistics#
Statistics not available.
Sample Example#
Sample example not available.
Prompt Template#
Prompt Template:
Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.
{question}
{choices}
Usage#
Using CLI#
evalscope eval \
--model YOUR_MODEL \
--api-url OPENAI_API_COMPAT_URL \
--api-key EMPTY_TOKEN \
--datasets general_vmcq \
--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=['general_vmcq'],
limit=10, # Remove this line for formal evaluation
)
run_task(task_cfg=task_cfg)