| --- |
| 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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|
