| --- |
| language: |
| - en |
| license: mit |
| task_categories: |
| - text-generation |
| - conversational |
| tags: |
| - human-collaboration |
| - grounding |
| - mental-model |
| - map-task |
| - multi-agent |
| pretty_name: Almanac |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: follower_next_action |
| data_files: |
| - split: train |
| path: data/sft/follower_next_action/train.jsonl |
| - split: test |
| path: data/sft/follower_next_action/test.jsonl |
| - config_name: guide_next_action |
| data_files: |
| - split: train |
| path: data/sft/guide_next_action/train.jsonl |
| - split: test |
| path: data/sft/guide_next_action/test.jsonl |
| - config_name: follower_mental_model |
| data_files: |
| - split: train |
| path: data/sft/follower_mental_model/train.jsonl |
| - split: test |
| path: data/sft/follower_mental_model/test.jsonl |
| - config_name: guide_mental_model |
| data_files: |
| - split: train |
| path: data/sft/guide_mental_model/train.jsonl |
| - split: test |
| path: data/sft/guide_mental_model/test.jsonl |
| - config_name: raw_timeline |
| data_files: |
| - split: train |
| path: data/raw_timeline/train.jsonl |
| - split: test |
| path: data/raw_timeline/test.jsonl |
| - config_name: grounding_timeline |
| data_files: |
| - split: train |
| path: data/grounding_timeline/train.jsonl |
| - split: test |
| path: data/grounding_timeline/test.jsonl |
| --- |
| |
| # Almanac |
|
|
| Human–human collaborative map-reproduction sessions for studying grounding acts, mental models, and next-action prediction in guide–follower teams. |
|
|
| ## Dataset contents |
|
|
| | Component | Location | Description | |
| |-----------|----------|-------------| |
| | **Raw sessions** | `data/raw_sessions/{c1,c2}/{study}/` | Per-session guide/follower timelines, full action logs, score boards | |
| | **SFT splits** | `data/sft/{task}/train.jsonl`, `test.jsonl` | Chat-format fine-tuning data for 4 prediction tasks | |
| | **Grounding** | `data/grounding/{c1,c2}/{study}/` | LLM-annotated grounding acts on each timeline step | |
| | **Flat tables** | `data/raw_timeline/`, `data/grounding_timeline/` | One row per timeline step; `train.jsonl` / `test.jsonl` plus `all.jsonl` | |
| | **Metadata** | `metadata/` | Split protocol, study index, grounding manifest | |
|
|
| ### Experimental conditions (canvas visibility) |
|
|
| Sessions are grouped by whether the guide can see the follower’s drawing canvas: |
|
|
| | Condition | Canvas visibility | Description | |
| |-----------|-------------------|-------------| |
| | **c1** | **Canvas not visible** | The follower’s canvas is hidden from the guide; the guide cannot see the follower’s drawing progress. | |
| | **c2** | **Canvas visible** | The follower’s canvas is visible to the guide; the guide can see the follower’s drawing progress. | |
|
|
| In flat tables and SFT metadata, `condition` / `canvas_condition` uses `c1` or `c2` accordingly. |
|
|
| ### SFT tasks |
|
|
| 1. `follower_next_action` / `guide_next_action` — predict the next action (message, draw, erase, undo, reset). |
| 2. `follower_mental_model` / `guide_mental_model` — predict mental-model fields at each step. |
|
|
| Each JSONL row includes `messages` (system / user / assistant), plus metadata: `task`, `role`, `canvas_condition`, `study_name`, `participant_name`, `timeline_index`, `step_index`. |
|
|
| ### Grounding annotations |
|
|
| Grounding labels (`grounding_act`, `grounding_rationale`) were generated with Azure OpenAI **gpt-5.5** using the project prompt in `Grounding Acts Human Annotation/llm_propmt.txt`. See `metadata/grounding_manifest.json` for per-study file paths and `metadata/studies_index.json` for action counts. |
|
|
| ## Train / test split (by study) |
|
|
| Splits are defined at the **session (study)** level, not by random steps. |
|
|
| **Train — c1:** study3, study9, study12, study14, study18, study24, study27, study30, study31 |
| **Train — c2:** study2, study4, study6, study10, study11, study13, study20, study22, study35, study37 |
|
|
| **Test — c1:** study8, study21, study25 |
| **Test — c2:** study15, study17, study36 |
|
|
| Full definition: `metadata/split_protocol.json`. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # SFT example |
| ds = load_dataset("NEU-HAI/Almanac", "follower_next_action", split="train") |
| print(ds[0]["messages"]) |
| |
| # Flat grounding timeline (study-level train/test splits) |
| g = load_dataset("NEU-HAI/Almanac", "grounding_timeline", split="test") |
| print(g[0]["grounding_act"], g[0]["action_content"]) |
| ``` |
|
|
| ## Regenerating this bundle |
|
|
| From the repository root: |
|
|
| ```bash |
| python Almanac/scripts/prepare_almanac.py |
| ``` |
|
|
| ## Upload to Hugging Face Hub |
|
|
| ```bash |
| hf auth login |
| cd Almanac |
| hf upload NEU-HAI/Almanac . --repo-type=dataset |
| ``` |
|
|
| For large updates or resumable uploads: |
|
|
| ```bash |
| hf upload-large-folder NEU-HAI/Almanac . --repo-type=dataset |
| ``` |
|
|
| ## License |
|
|
| This dataset is released under the [MIT License](LICENSE). |
|
|
| ## Citation |
|
|
| If you use Almanac, please cite the associated EMNLP 2026 paper (bibtex to be added). |
|
|
