MIA-Bench

Overview

MIA-Bench is a multimodal instruction-following benchmark designed to evaluate vision-language models on their ability to follow complex, compositional instructions grounded in images. Each sample contains an image paired with a multi-component instruction, and model responses are scored by an LLM judge per component.

Task Description

• Task Type: Multimodal Instruction Following

• Input: Image + multi-component instruction

• Output: Free-form response following all instruction components

• Domains: Visual understanding, instruction following, language generation

Key Features

• 400 test samples with diverse instruction types (basic to advanced)

• Each instruction decomposes into 1–5 graded components with weighted scores

• Component types include: describe, length_limit, linguistics, format, etc.

• LLM-as-judge scoring: judge evaluates each component independently and gives a weighted total score (0–10 range, normalized to 0–1)

• No predefined reference answers; scoring is fully judge-based

Evaluation Notes

• Default evaluation uses the test split (400 samples)

• Primary metric: total_score (mean of per-sample normalized 0–1 total scores)

• Requires a capable LLM judge (e.g., GPT-4o, Qwen-Max) configured via judge_model_args

• Judge strategy should be set to JudgeStrategy.LLM

Properties

Property	Value
Benchmark Name	mia_bench
Dataset ID	lmms-lab/MIA-Bench
Paper	N/A
Tags	InstructionFollowing, MultiModal, QA
Metrics	total_score
Default Shots	0-shot
Evaluation Split	test

Data Statistics

Metric	Value
Total Samples	400
Prompt Length (Mean)	137.81 chars
Prompt Length (Min/Max)	34 / 327 chars

Image Statistics:

Metric	Value
Total Images	400
Images per Sample	min: 1, max: 1, mean: 1
Resolution Range	135x240 - 3264x4928
Formats	jpeg, mpo, webp

Sample Example

Subset: default

{
  "input": [
    {
      "id": "9156e81c",
      "content": [
        {
          "image": "[BASE64_IMAGE: jpeg, ~195.9KB]"
        },
        {
          "text": "Explain the activity taking place in the image using exactly two sentences, including one metaphor."
        }
      ]
    }
  ],
  "target": "",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "instruction": "Explain the activity taking place in the image using exactly two sentences, including one metaphor.",
    "type": "advanced",
    "num_of_component": 3,
    "components": [
      "Explain the activity taking place in the image",
      "using exactly two sentences",
      "including one metaphor"
    ],
    "component_weight": [
      4,
      3,
      3
    ],
    "component_type": [
      "describe",
      "length_limit",
      "linguistics"
    ]
  }
}

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 mia_bench \
    --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=['mia_bench'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)