Hugging Face's logo

Datasets:
KORMo-VL
/
EVIDENT

Modalities:
Text
Formats:
json
Languages:
Korean
English
Size:
< 1K
Tags:
gui-agent
web-agent
evaluation
evidence-grounded
Libraries:
Datasets
pandas
Polars
Dataset card Data Studio Files
xet
 Community
1
EVIDENT / README.md
JLee0's picture
JLee0
Correct contributor email labels to KAIST addresses
5ab233c verified
preview code
|
Raw History Blame Contribute Delete
3.61 kB
metadata 
language:
  - ko
  - en
tags:
  - gui-agent
  - web-agent
  - evaluation
  - evidence-grounded
configs:
  - config_name: ko
    data_files:
      - split: test
        path: tasks/150_ko_metadata.json
  - config_name: en
    data_files:
      - split: test
        path: tasks/70_en_metadata.json

EVIDENT: Task Metadata for Evidence-Grounded GUI Evaluation

GitHub

This release contains 220 GUI/web agent task records for evidence-grounded trajectory evaluation: 150 Korean and 70 English tasks. Task definitions pair a user request with its intended outcome and evidence checks.

Evaluation code and instructions: yerinNam/EVIDENT.

Task type Korean English Total
Semantic Grounding 50 27 77
Contextual Inference 50 21 71
Visual Grounding 50 22 72
Total 150 70 220

Contents

  • tasks/150_ko_metadata.json: 150 Korean task records.
  • tasks/70_en_metadata.json: 70 English task records.

Both files are JSON arrays. Every record includes id, prompt, task_type, start_url, user_intent, and evidence_checks (a list of checks). Some records also include visual_target (a string) or visual_cues (a list). IDs are unique within each language file. These are task metadata files; agent trajectories and screenshots must be collected separately.

Download 

from huggingface_hub import snapshot_download

snapshot_download("KORMo-VL/EVIDENT", repo_type="dataset", local_dir="EVIDENT-data")

Evaluate agent trajectories

EVIDENT implements three stages: extract observed screenshot evidence, review final-answer claims against that evidence, and judge the task outcome with task metadata and rubrics.

Follow the GitHub README to install dependencies and start the judge server. Then, from the cloned EVIDENT repository, run:

python3 -m pipeline.main_eval \
  --tasks-json /path/to/EVIDENT-data/tasks/150_ko_metadata.json \
  --results-root /path/to/agent_results \
  --port 8005

Use 70_en_metadata.json for English. The results directory should contain one episode directory per task ID, with trajectory.json and the screenshots it references. See the repository README for single-episode evaluation, final-answer inputs, cache options, and reports.

These tasks use live websites. Content, availability, prices, and rankings may change; record the evaluation date when reporting results.

About KORMo-VL

KORMo-VL is a vision-language model developed from scratch by the KAIST MLP Lab, built on top of KORMo-10B. This task metadata release supports GUI/web agent evaluation under the KORMo-VL organization.

Source

The task JSON files are preserved byte-for-byte from yerin/EVIDENT/tasks. Evaluation implementation: yerinNam/EVIDENT.

Contact

  • KyungTae Lim, Professor at KAIST. ktlim@kaist.ac.kr

Contributor