| --- |
| configs: |
| - config_name: character |
| data_files: |
| - split: train |
| path: sync_characters.jsonl |
|
|
| - config_name: question |
| data_files: |
| - split: train |
| path: sync_questions.jsonl |
|
|
| - config_name: dialogue |
| data_files: |
| - split: train |
| path: sync_dialogues.jsonl |
|
|
| --- |
| # Dataset Card |
|
|
| ## Overview |
| This dataset is proposed in our work, "When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills". |
| It includes simulated persona grounded user–assistant dialogues as personal traces for skill distillation, consists of: |
| 1. `sync_characters.jsonl` contains 50 character profiles. |
| 2. `sync_questions.jsonl` contains 2,500 character-grounded questions. Each character has 50 questions: 30 general, 10 math, and 10 tool-use questions. |
| 3. `sync_dialogues.jsonl` contains multi-turn dialogues built from those questions. GPT-5.4 simulates the user, while GPT-5.4-mini simulates the assistant with more concise responses. |
|
|
|
|
| ## Detailed File Descriptions |
|
|
| ### `sync_characters.jsonl` |
| |
| Each record describes one character, including a unique ID, a persona summary, personality and language-style descriptions, a longer natural-language character description, and structured profile attributes such as age, gender, occupation, and education. |
| |
| ### `sync_questions.jsonl` |
|
|
| Each record contains `character_id`, `question_id`, `question_type`, and `question`. Question types are labeled `general_question`, `math_question`, and `tool_question`. |
|
|
| ### `sync_dialogues.jsonl` |
| |
| Each record contains `character_id`, the source `question`, and a `dialogue` list. Every dialogue turn has a `role` and `content` field. |
|
|
| ## Citation |
| If you find this dataset useful for your research, please consider citing our paper: |
| ``` |
| @misc{xiang2026antiskillbench, |
| title={When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills}, |
| author={Yongli Xiang and Zhifang Zhang and Bojun Yang and Ziming Hong and Lei Feng and Miao Xu and Tongliang Liu}, |
| year={2026}, |
| eprint={2608.03700}, |
| archivePrefix={arXiv}, |
| url={https://arxiv.org/abs/2608.03700}, |
| } |
| ``` |
|
|
