| --- |
| 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 |
|
|
| [](https://github.com/yerinNam/EVIDENT) |
|
|
| 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](https://github.com/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 |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download("KORMo-VL/EVIDENT", repo_type="dataset", local_dir="EVIDENT-data") |
| ``` |
|
|
| ## Evaluate agent trajectories |
|
|
| [EVIDENT](https://github.com/yerinNam/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: |
|
|
| ```bash |
| 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](https://github.com/yerinNam/EVIDENT#usage) |
| 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](https://sites.google.com/view/aailab), built on top of KORMo-10B. |
| This task metadata release supports GUI/web agent evaluation under the |
| [KORMo-VL organization](https://huggingface.co/KORMo-VL). |
|
|
| ## Source |
|
|
| The task JSON files are preserved byte-for-byte from `yerin/EVIDENT/tasks`. |
| Evaluation implementation: [yerinNam/EVIDENT](https://github.com/yerinNam/EVIDENT). |
|
|
| ## Contact |
|
|
| * KyungTae Lim, Professor at KAIST. `ktlim@kaist.ac.kr` |
|
|
| ## Contributor |
|
|
| * Yerin Nam ([yerinnam@kaist.ac.kr](mailto:yerinnam@kaist.ac.kr)) |
| * Minkyung Cho ([minkyung.cho@kaist.ac.kr](mailto:minkyung.cho@kaist.ac.kr)) |
| * Minkyu Kim ([minkyukim@kaist.ac.kr](mailto:minkyukim@kaist.ac.kr)) |
| * Seungwoo Song ([sswoo@kaist.ac.kr](mailto:sswoo@kaist.ac.kr)) |
| * Hangyeol Yoo ([hgyoo@kaist.ac.kr](mailto:hgyoo@kaist.ac.kr)) |
| * KyungTae Lim ([ktlim@kaist.ac.kr](mailto:ktlim@kaist.ac.kr)) |
|
|
