| --- |
| language: |
| - zh |
| task_categories: |
| - text-classification |
| pretty_name: VARM-Bench |
| size_categories: |
| - 1K<n<10K |
| tags: |
| - abusive-language-detection |
| - content-moderation |
| - structured-reasoning |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train.jsonl |
| - split: validation |
| path: data/dev.jsonl |
| - split: test |
| path: data/test.jsonl |
| --- |
| |
| # VARM-Bench |
|
|
| VARM-Bench is a Chinese abusive-speech moderation benchmark for evaluating verifiable structured reasoning. Each record links one natural-language rationale to six moderation decisions: target, target type, target explicitness, author stance, harmfulness label, and fine-grained category. |
|
|
| ## Content warning |
|
|
| The dataset contains abusive, discriminatory, offensive, and vulgar language. Some examples may be disturbing. Use appropriate safeguards when displaying raw records or model outputs. |
|
|
| ## Dataset splits |
|
|
| | Split | Rows | |
| | --- | ---: | |
| | Train | 5,600 | |
| | Validation | 800 | |
| | Test | 1,600 | |
| | Total | 8,000 | |
|
|
| The release contains 4,400 harmful and 3,600 non-harmful records. The main splits have no normalized text overlap. |
|
|
| ## Fields |
|
|
| Each JSONL record contains eight fields: |
|
|
| - `text`: normalized Chinese social-media text. |
| - `target`: shortest stable referent for the decision-relevant proposition. |
| - `target_type`: `单一对象`, `群体对象`, or `无明确对象`. |
| - `target_explicitness`: `明示`, `隐含`, or `无明确对象`. |
| - `stance`: `攻击或认同`, `反对攻击`, `引用或转述`, or `中性提及`. |
| - `label`: `有害` or `无害`. |
| - `category`: `非攻击`, `一般辱骂`, `地域族群攻击`, `性别攻击`, `性少数攻击`, `阶层职业攻击`, or `身心疾病攻击`. |
| - `cot`: a natural-language rationale containing the ordered anchors `[T:]`, `[TY:]`, `[TT:]`, `[S:]`, `[L:]`, and `[C:]`. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("1nvis1ble/VARM-Bench") |
| print(dataset) |
| ``` |
|
|
| The primary files are under `data/`. The `subsets/` directory also includes: |
|
|
| - 1,440 difficult non-harmful records split across train, validation, and test. |
| - Four overlapping lexical-cue subsets for demographic identity, social status or role, body, health or disability, and general abuse. |
|
|
| ## Intended use |
|
|
| VARM-Bench supports research on Chinese abusive-speech moderation, structured prediction, rationale parsing, and field-level error analysis. It is an evaluation resource, not an automated moderation policy or a substitute for human review. |
|
|
| ## Data processing |
|
|
| The source texts were normalized and filtered for self-contained content. URLs, markup, and unresolved personal identifiers were removed or generalized. All released records passed schema, anchor-order, cross-field consistency, duplicate, and cross-split overlap checks. |
|
|
| ## Limitations |
|
|
| The dataset reflects its Chinese social-media sources and annotation policy. It does not represent all dialects, communities, platforms, contexts, or moderation standards. Rationales provide inspectable task annotations; they do not reveal a model's latent reasoning process. |
|
|
| ## Authors |
|
|
| Mingyu Yuan, Shengtao Wen, Lingbing Guo, Zhen Bi, and Xiang Chen. |
|
|
| ## Citation |
|
|
| Please cite the VARM-Bench paper. Formal arXiv citation metadata will be added after the preprint is available. |
|
|
