Restore benchmark context in dataset card

README.md

@@ -28,7 +28,14 @@ tags:
 
 # OmniToM
 
-OmniToM is a benchmark for evaluating how language models represent what people know and believe in short social stories. Each story has actor-centered belief statements. Each statement has labels for seven aspects of the belief, such as its truth status, who can access the information, and how the actor learned it. The dataset supports two tasks: extracting beliefs from a story and labeling those beliefs.
+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 also records the beliefs behind that answer, so researchers can inspect what the model thinks each person knows, believes, or misunderstands.
+
+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 and their belief statements.
+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
 
@@ -38,6 +45,7 @@ OmniToM is a benchmark for evaluating how language models represent what people
 | Story categories | 7 |
 | Belief statements | 22,343 |
 | Labels per belief | 7 |
+| Total belief labels | 156,401 |
 
 The stories come from seven ToMBench categories:
 
@@ -50,10 +58,11 @@ The stories come from seven ToMBench categories:
 | Persuasion Story Task | 97 |
 | Scalar Implicature Test | 154 |
 | Strange Story Task | 207 |
+| **Total** | **895** |
 
 ## Data Fields
 
-Each row in `benchmark_story_belief_labels.jsonl` is one story. The `beliefs` list contains its annotated belief statements.
+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 |
 | --- | --- |
@@ -80,11 +89,13 @@ stories = load_dataset("BridgeAI-Lab/omnitom-benchmark", split="train")
 print(stories[0])
 ```
 
-Hugging Face calls the single released benchmark split `train` for loading purposes. The dataset is intended for evaluation and analysis, not as a recommended training set. The [code repository](https://github.com/BridgeAI-Lab/OMNITOM) contains the evaluation scripts.
+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 the human-validated, LLM-assisted annotation process described in the [OmniToM paper](https://arxiv.org/abs/2605.26322). OmniToM covers English, text-only, short stories. Results should not be taken as a measure of general real-world social reasoning or deployment safety.
+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