data

If you are looking for our intermediate labeling version, please refer to mango-ttic/data-intermediate

Find more about us at mango.ttic.edu

Folder Structure

Each folder inside data contains the cleaned up files used during LLM inference and results evaluations. Here is the tree structure from game data/night .

data/night/
├── night.actions.json      # list of mentioned actions
├── night.all2all.jsonl      # all simple paths between any 2 locations
├── night.all_pairs.jsonl    # all connectivity between any 2 locations
├── night.edges.json        # list of all edges
├── night.locations.json    # list of all locations
└── night.walkthrough       # enriched walkthrough exported from Jericho simulator

Variations

70-step vs all-step version

In our paper, we benchmark using the first 70 steps of the walkthrough from each game. We also provide all-step versions of both data and data-intermediate collection.

70-step data-70steps.tar.zst: contains the first 70 steps of each walkthrough. If the complete walkthrough is shorter than 70 steps, then all steps are used.
All-step data.tar.zst: contains all steps of each walkthrough.

Word-only & Word+ID

Word-only data.tar.zst: Nodes are annotated by additional descriptive text to distinguish different locations with similar names.
Word + Object ID data-objid.tar.zst: variation of the word-only version, where nodes are labeled using minimaly fixed names with object id from Jericho simulator.
Word + Random ID data-randid.tar.zst: variation of the Jericho ID version, where the Jericho object id replaced with randomly generated integer.

We primarily rely on the word-only version as benchmark, yet providing word+ID version for diverse benchmark settings.

How to use

We use data.tar.zst as an example here.

1. download from Huggingface

by directly download

You can selectively download certain variation of your choice.

by git

Make sure you have git-lfs installed

git lfs install
git clone https://huggingface.co/datasets/mango-ttic/data

# or, use hf-mirror if your connection to huggingface.co is slow
# git clone https://hf-mirror.com/datasets/mango-ttic/data

If you want to clone without large files - just their pointers

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/mango-ttic/data

# or, use hf-mirror if your connection to huggingface.co is slow
# GIT_LFS_SKIP_SMUDGE=1 git clone https://hf-mirror.com/datasets/mango-ttic/data

2. decompress

Because some json files are huge, we use tar.zst to package the data efficiently.

silently decompress

tar -I 'zstd -d' -xf data.tar.zst

or, verbosely decompress

zstd -d -c data.tar.zst | tar -xvf -