README.md

---
language:
- zh
pretty_name: Chinese Textual Ambiguity Dataset
size_categories:
- n<1K
task_categories:
- text-classification
- text-generation
tags:
- chinese
- textual-ambiguity
- disambiguation
- ambiguity-detection
- llm-evaluation
- linguistics
---

# Chinese Textual Ambiguity Dataset

This dataset is the accompanying dataset for the paper:

**Uncovering the Fragility of Trustworthy LLMs through Chinese Textual Ambiguity**

- Paper (arXiv): https://arxiv.org/abs/2507.23121
- Project repository: https://github.com/ictup/LLM-Chinese-Textual-Disambiguation

## Dataset Summary

This release contains **925 Chinese textual ambiguity records** collected and annotated for research on ambiguity detection, ambiguity understanding, disambiguation, and trustworthy handling of ambiguous Chinese inputs.

The public release contains the human-facing dataset fields only. Internal annotation/review workflow fields and model prediction outputs have been removed.

There is currently no predefined train/validation/test partition in this release. `data.csv` contains the complete released corpus.

## Data Fields

The dataset contains 13 fields:

- `歧义句`: ambiguous sentence
- `歧义句及上下文`: ambiguous sentence together with its context
- `歧义文本位置`: span or expression responsible for the ambiguity
- `歧义原因（解读选项）`: explanation of the ambiguity and plausible interpretations
- `歧义句消岐1`: first disambiguated rewrite
- `歧义句消岐2`: second disambiguated rewrite
- `歧义句消岐3`: third disambiguated rewrite, where available
- `歧义句消岐4`: fourth disambiguated rewrite, where available
- `绝对索引`: source record index
- `歧义类型`: human-annotated ambiguity category
- `歧义内容语体类型`: register/style annotation
- `歧义内容所属领域`: domain annotation
- `母语者消歧难度`: native-speaker disambiguation difficulty annotation

## Intended Uses

This dataset is intended for research on:

- Chinese textual ambiguity detection
- Ambiguity source localization
- Enumeration of plausible interpretations
- Explicit disambiguation and rewriting
- Evaluation of LLM uncertainty handling and trustworthy response behavior

## Limitations

Chinese textual ambiguity is context-sensitive and can admit multiple defensible interpretations. Some annotation fields may be unavailable for individual records. Researchers should inspect the relevant fields before defining task-specific experimental splits or evaluation protocols.

## License

The authors should select an explicit dataset license before public release. The license is intentionally not declared in the metadata of this draft Dataset Card.

## Citation

If you use this dataset, please cite the associated paper:

```bibtex
@misc{wu2025uncoveringfragilitytrustworthyllms,
  title        = {Uncovering the Fragility of Trustworthy LLMs through Chinese Textual Ambiguity},
  author       = {Xinwei Wu and Haojie Li and Hongyu Liu and Xinyu Ji and Ruohan Li and Yule Chen and Yigeng Zhang},
  year         = {2025},
  eprint       = {2507.23121},
  archivePrefix= {arXiv},
  primaryClass = {cs.CL},
  url          = {https://arxiv.org/abs/2507.23121}
}
```

## Contact

For questions, please use the project repository:
https://github.com/ictup/LLM-Chinese-Textual-Disambiguation