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:
- Belief extraction: Given a story, identify the actors, their belief statements, and the order of each belief.
- 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 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
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 contains the evaluation scripts.
Source and Limitations
The stories are drawn from ToMBench. The belief statements and labels were produced through an LLM-assisted process with human validation and adjudication, as described in the OmniToM paper. 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
@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}
}
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