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pretty_name: OmniToM
language:
- en
license: mit
task_categories:
- text-generation
- text-classification
task_ids:
- text2text-generation
- multi-class-classification
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: benchmark_story_belief_labels.jsonl
tags:
- theory-of-mind
- benchmark
- social-reasoning
- belief-modeling
- llm-evaluation
- cognitive-reasoning
- mental-state-representation
---
# OmniToM
OmniToM is a benchmark for evaluating Theory of Mind in language models through explicit belief-structure modeling. Many benchmarks ask a model to answer a question about a social story. OmniToM provides annotated belief structures for the stories, so researchers can compare a model's extracted beliefs with what the benchmark records about each person.
Each example pairs a short English story with actor-centered belief statements and seven labels for each belief. The benchmark supports two linked tasks:
1. **Belief extraction:** Given a story, identify the actors, their belief statements, and the order of each belief.
2. **Belief labeling:** Given a story and its belief statements, assign the seven schema labels to each belief.
The released data contains the annotated stories and beliefs. The [paper](https://arxiv.org/abs/2605.26322) explains the benchmark design and evaluation protocol.
## Dataset Summary
| Measure | Count |
| --- | ---: |
| Stories | 895 |
| Story categories | 7 |
| Belief statements | 22,343 |
| Labels per belief | 7 |
| Total belief labels | 156,401 |
The stories come from seven ToMBench categories:
| Story category | Stories |
| --- | ---: |
| Ambiguous Story Task | 98 |
| False Belief Task | 97 |
| Faux-pas Recognition Test | 142 |
| Hinting Task Test | 100 |
| Persuasion Story Task | 97 |
| Scalar Implicature Test | 154 |
| Strange Story Task | 207 |
| **Total** | **895** |
## Data Fields
The release contains one JSONL file, `benchmark_story_belief_labels.jsonl`. Each line is one story. Its `beliefs` list contains the belief statements and their labels. The reserved actor `world` is used for facts stated by the narrator rather than beliefs held by a character.
| Field | Description |
| --- | --- |
| `story_id` | Unique story ID. |
| `story_category` | ToMBench story category. |
| `story` | Story text. |
| `beliefs` | List of annotated belief statements. |
| `beliefs[].actor` | Belief holder. `world` denotes a narrator or world fact. |
| `beliefs[].belief` | Belief statement. |
| `beliefs[].labels.order` | Belief depth: `0`, `1`, `2`, or `3`. |
| `beliefs[].labels.truth_status` | `True`, `False`, or `Unknown`. |
| `beliefs[].labels.knowledge_access` | `Private`, `Shared`, or `Public`. |
| `beliefs[].labels.representation` | `Explicit` or `Implicit`. |
| `beliefs[].labels.content_type` | `Location`, `Contents/Physical State`, `Identity/Relation`, `Epistemic`, `Desire/Intention`, `Emotion`, `Trait/Value`, or `Action/Event`. |
| `beliefs[].labels.mental_source` | `Narration`, `Perception`, `Memory`, `Testimony`, `Inference`, `Imagination`, or `Unknown`. |
| `beliefs[].labels.context` | `Neutral`, `Temporal`, `Deceptive`, or `Counterfactual`. |
## Usage
```python
from datasets import load_dataset
stories = load_dataset("BridgeAI-Lab/omnitom-benchmark", split="train")
print(stories[0])
```
Hugging Face calls the single released benchmark split `train` for loading purposes. It is the benchmark split, not a recommended training set. The [code repository](https://github.com/BridgeAI-Lab/OMNITOM) contains the evaluation scripts.
## Source and Limitations
The stories are drawn from [ToMBench](https://github.com/zhchen18/ToMBench). The belief statements and labels were produced through an LLM-assisted process with human validation and adjudication, as described in the [OmniToM paper](https://arxiv.org/abs/2605.26322). This release contains the seven retained story categories, rather than the full ToMBench collection.
OmniToM covers English, text-only, short stories. Its labels may reflect judgment calls in ambiguous social situations. The dataset is best used to study belief representations in these stories, not as a general measure of real-world social reasoning.
## Citation and Resources
```bibtex
@misc{omnitom2026,
title={OmniToM: Benchmarking Theory of Mind in LLMs via Explicit Belief Modeling},
author={Bawatneh, Adam and Sapkota, Sagar and Bedi, Amrit Singh and Karmaker, Santu and Shah, Mubarak},
year={2026},
eprint={2605.26322},
archivePrefix={arXiv}
}
```
[Project page](https://adam-12-0.github.io/omnitom-project/) · [Code](https://github.com/BridgeAI-Lab/OMNITOM) · [Croissant metadata with Responsible AI fields](https://huggingface.co/datasets/BridgeAI-Lab/omnitom-benchmark/resolve/main/croissant_rai_omnitom_camera_ready.json)
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