README.md

metadata

pretty_name: MIRAGE
license: cc-by-sa-4.0
task_categories:
  - image-text-to-text
  - visual-question-answering
size_categories:
  - 10K<n<100K
source_datasets:
  - AskExtension
configs:
  - config_name: MMMT_Direct
    default: true
    data_files:
      - split: train
        path: MMMT_Direct/train-0000-of-0001.parquet
      - split: dev
        path: MMMT_Direct/dev-0000-of-0001.parquet
      - split: test
        path: MMMT_Direct/test-0000-of-0001.parquet
  - config_name: MMMT_Decomp
    data_files:
      - split: train
        path: MMMT_Decomp/train-0000-of-0001.parquet
      - split: dev
        path: MMMT_Decomp/dev-0000-of-0001.parquet
      - split: test
        path: MMMT_Decomp/test-0000-of-0001.parquet
  - config_name: MMST_Standard
    data_files:
      - split: train
        path: MMST_Standard/train-0000-of-0001.parquet
      - split: test
        path: MMST_Standard/test-0000-of-0001.parquet
  - config_name: MMST_Contextual
    data_files:
      - split: test
        path: MMST_Contextual/test-0000-of-0001.parquet
dataset_info:
  - config_name: MMMT_Direct
    features: &ref_0
      - name: images
        list:
          - name: bytes
            dtype: 'null'
          - name: path
            dtype: string
      - name: id
        dtype: string
      - name: messages
        list:
          - name: role
            dtype: string
          - name: content
            list:
              - name: type
                dtype: string
              - name: text
                dtype: string
      - name: raw_metadata
        dtype: string
    splits:
      - name: train
        num_bytes: 3734905
        num_examples: 3876
      - name: dev
        num_bytes: 844393
        num_examples: 878
      - name: test
        num_bytes: 844960
        num_examples: 861
    download_size: 1325714410
    dataset_size: 1325714410
  - config_name: MMMT_Decomp
    features: *ref_0
    splits:
      - name: train
        num_bytes: 4407679
        num_examples: 3876
      - name: dev
        num_bytes: 995362
        num_examples: 878
      - name: test
        num_bytes: 992906
        num_examples: 861
    download_size: 1326686099
    dataset_size: 1326686099
  - config_name: MMST_Standard
    features: *ref_0
    splits:
      - name: train
        num_bytes: 17348634
        num_examples: 17532
      - name: test
        num_bytes: 7962774
        num_examples: 8184
    download_size: 6948107240
    dataset_size: 6948107240
  - config_name: MMST_Contextual
    features: *ref_0
    splits:
      - name: test
        num_bytes: 6599964
        num_examples: 3934
    download_size: 1341825286
    dataset_size: 1341825286

MIRAGE

MIRAGE is a benchmark built from real conversations between farmers or gardeners and agricultural experts on the AskExtension platform. The authors collected about 285,000 consultations from 2012 to 2025 and kept 37,512 high-quality ones, covering more than 7,600 plants, pests and diseases. Many questions leave out key details, so a good model has to notice what is missing and ask, not just answer.

The authors tested 22 vision-language models. Even the best, GPT-4.1, reached only 43.9% identification accuracy.

• Paper: MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations (NeurIPS 2025 Datasets and Benchmarks)
• Project page: mirage-benchmark.github.io
• Original dataset: MIRAGE-Benchmark/MIRAGE

Configs

Config	What it is	Train	Dev	Test
MMST_Standard	Single-turn questions that can be answered from the text and images	17,532		8,184
MMST_Contextual	Single-turn questions with missing context (place, time or background) the model should notice			3,934
MMMT_Direct	Multi-turn dialogues where the model decides to ask a clarifying question or answer, then writes it	3,876	878	861
MMMT_Decomp	The same dialogues, with the known and missing goals spelled out	3,876	878	861

Structure

MMST_Standard/
  train-0000-of-0001.parquet
  test-0000-of-0001.parquet
  images/
    image-mmst_standard-000-of-003.zip ... image-mmst_standard-002-of-003.zip
    path_to_shard.parquet
MMST_Contextual/
  test-0000-of-0001.parquet
  images/
MMMT_Direct/
  train-0000-of-0001.parquet
  dev-0000-of-0001.parquet
  test-0000-of-0001.parquet
  images/
MMMT_Decomp/
  (same as MMMT_Direct)
Source_Data_For_MMMT.json

Each config has its own images/ folder. Unique images per config: MMST_Standard 46,874, MMST_Contextual 8,032, MMMT_Direct and MMMT_Decomp 7,995 each.

Each row has:

• images: list of {"bytes": null, "path": <sha256>}, up to 3 per row, in the source's image_1, image_2, image_3 order. The path is the SHA256 of the image bytes and points into a zip shard through that config's images/path_to_shard.parquet.
• id: mirage_<config>_<split>_<counter>, for example mirage_mmst_standard_test_0.
• messages: one user turn (images, then text) and one assistant turn.
  • In the MMST configs, the text is the question and the assistant turn is the expert's answer.
  • In the MMMT configs, the text is the dialogue so far (dialog_context, with user: and expert: lines), and the assistant turn is the next expert utterance.
• raw_metadata: JSON string.
  • MMST: original_id, subset, answer_type, category, entity_type, entity_name, entity_scientific_name, entity_common_names, state, county and asked_time. MMST_Contextual also has location_related, time_related, location_related_analysis and time_related_analysis.
  • MMMT: original_id, subset, answer_type, decision (such as <Clarify>), dialog_turns, known_goal and missing_goal.

Example (texts shortened):

{
  "images": [{"bytes": null, "path": "cbff90a5ba89a76488f0533e5a98dc2a33095785a299e90eb7c09664a058e773"}],
  "id": "mirage_mmst_standard_test_0",
  "messages": [
    {"role": "user", "content": [
      {"type": "image", "text": null},
      {"type": "text", "text": "My arborvitae's have little gold, looking seeds on the ends and are also turning brown. Part of it m..."}
    ]},
    {"role": "assistant", "content": [{"type": "text", "text": "The seeds are just that. Just like some get pine cones, this is the arborvitae's..."}]}
  ],
  "raw_metadata": "{\"original_id\": \"#885475\", \"subset\": \"MMST_Standard\", \"answer_type\": \"oeqa\", \"category\": \"Plant Care and Gardening Guidance\", \"entity_type\": \"plant\", \"entity_name\": \"arborvitae\", \"entity_scientific_name\": \"\", \"entity_common_names\": [], \"state\": \"Minnesota\", \"county\": \"Stearns County\", \"asked_time\": \"2024-09-16 16:35:00\"}"
}

Changes from the original data

• Each source config becomes a config here, with its own source splits. dev keeps its source name.
• image_1, image_2 and image_3 become the images list. Empty slots are left out.
• The MMMT dialogue history stays as one block of text in the user turn, exactly as in dialog_context. It is not split into separate turns.
• Some source fields are renamed in raw_metadata: id becomes original_id, and meta_data_state, meta_data_county and meta_data_asked_time become state, county and asked_time.
• raw_metadata also has two fields that are not in the source:
  • subset: the config name.
  • answer_type: added during conversion. It is mcqa when the question lists at least two lettered or numbered options, or the answer is a single letter or digit, and oeqa otherwise. MMMT rows are always oeqa.
• MMMT_Direct rows have known_goal and missing_goal set to null, since only MMMT_Decomp has them in the source.
• Images are stored once per config, deduplicated by SHA256. MMMT_Direct and MMMT_Decomp share the same images, so each holds its own copy.
• Source_Data_For_MMMT.json from the original dataset is included unchanged.

Usage

from agml import loadImageTextToTextDataset

dataset = loadImageTextToTextDataset("Project-AgML/MIRAGE", config="MMST_Standard")

sample = dataset["test"][0]
sample["images"]    # list of PIL images
sample["messages"]

Citation

@article{dongre2025mirage,
  title={MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations},
  author={Dongre, Vardhan and Gui, Chi and Garg, Shubham and Nayyeri, Hooshang and Tur, Gokhan and Hakkani-T{\"{u}}r, Dilek and Adve, Vikram S},
  journal={arXiv preprint arXiv:2506.20100},
  year={2025}
}

This dataset is indexed and structured on https://project-agml.github.io/ as part of Project AgML @ UC Davis. This dataset was reformatted from its original format to match HuggingFace's Imagefolder standards but requires an external module (agml) that processes and returns a HF Dataset object faster than HF module functions.