Datasets:
messages listlengths 3 3 | task stringclasses 1
value | role stringclasses 1
value | canvas_condition stringclasses 2
values | study_name stringclasses 19
values | participant_name stringclasses 19
values | timeline_index int64 0 347 | step_index int64 0 254 |
|---|---|---|---|---|---|---|---|
[
{
"role": "system",
"content": "<Task Description>\nYou are simulating a human participant’s action-level mental model in a two-player collaborative map-reproduction task.\n\nIn this task, there are two roles: guide and follower. The guide can see a map with all landmarks and the correct route. The follower... | mental_model_prediction | follower | c1 | study3 | Jesse | 0 | 0 |
[
{
"role": "system",
"content": "<Task Description>\nYou are simulating a human participant’s action-level mental model in a two-player collaborative map-reproduction task.\n\nIn this task, there are two roles: guide and follower. The guide can see a map with all landmarks and the correct route. The follower... | mental_model_prediction | follower | c1 | study3 | Jesse | 3 | 1 |
[
{
"role": "system",
"content": "<Task Description>\nYou are simulating a human participant’s action-level mental model in a two-player collaborative map-reproduction task.\n\nIn this task, there are two roles: guide and follower. The guide can see a map with all landmarks and the correct route. The follower... | mental_model_prediction | follower | c1 | study3 | Jesse | 4 | 2 |
[
{
"role": "system",
"content": "<Task Description>\nYou are simulating a human participant’s action-level mental model in a two-player collaborative map-reproduction task.\n\nIn this task, there are two roles: guide and follower. The guide can see a map with all landmarks and the correct route. The follower... | mental_model_prediction | follower | c1 | study3 | Jesse | 5 | 3 |
[
{
"role": "system",
"content": "<Task Description>\nYou are simulating a human participant’s action-level mental model in a two-player collaborative map-reproduction task.\n\nIn this task, there are two roles: guide and follower. The guide can see a map with all landmarks and the correct route. The follower... | mental_model_prediction | follower | c1 | study3 | Jesse | 7 | 4 |
[
{
"role": "system",
"content": "<Task Description>\nYou are simulating a human participant’s action-level mental model in a two-player collaborative map-reproduction task.\n\nIn this task, there are two roles: guide and follower. The guide can see a map with all landmarks and the correct route. The follower... | mental_model_prediction | follower | c1 | study3 | Jesse | 8 | 5 |
[
{
"role": "system",
"content": "<Task Description>\nYou are simulating a human participant’s action-level mental model in a two-player collaborative map-reproduction task.\n\nIn this task, there are two roles: guide and follower. The guide can see a map with all landmarks and the correct route. The follower... | mental_model_prediction | follower | c1 | study3 | Jesse | 10 | 6 |
[
{
"role": "system",
"content": "<Task Description>\nYou are simulating a human participant’s action-level mental model in a two-player collaborative map-reproduction task.\n\nIn this task, there are two roles: guide and follower. The guide can see a map with all landmarks and the correct route. The follower... | mental_model_prediction | follower | c1 | study3 | Jesse | 11 | 7 |
[
{
"role": "system",
"content": "<Task Description>\nYou are simulating a human participant’s action-level mental model in a two-player collaborative map-reproduction task.\n\nIn this task, there are two roles: guide and follower. The guide can see a map with all landmarks and the correct route. The follower... | mental_model_prediction | follower | c1 | study3 | Jesse | 13 | 8 |
[
{
"role": "system",
"content": "<Task Description>\nYou are simulating a human participant’s action-level mental model in a two-player collaborative map-reproduction task.\n\nIn this task, there are two roles: guide and follower. The guide can see a map with all landmarks and the correct route. The follower... | mental_model_prediction | follower | c1 | study3 | Jesse | 14 | 9 |
YAML Metadata Warning:The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
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
follower_next_action/guide_next_action— predict the next action (message, draw, erase, undo, reset).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
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:
python Almanac/scripts/prepare_almanac.py
Upload to Hugging Face Hub
hf auth login
cd Almanac
hf upload NEU-HAI/Almanac . --repo-type=dataset
For large updates or resumable uploads:
hf upload-large-folder NEU-HAI/Almanac . --repo-type=dataset
License
This dataset is released under the MIT License.
Citation
If you use Almanac, please cite the associated EMNLP 2026 paper (bibtex to be added).
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