PopQA_robustness

# Dataset Card for "PopQA-robustness"

### Dataset Summary

PopQS-robustness is an expanded version of the PopQA dataset (https://aclanthology.org/2023.acl-long.546/) but with perturbations of the original input questions.
It is intended for use as a benchmark for evaluating model robustness on question-answering to these perturbations.

### Data Instances

#### popqa_robustness

- **Size of downloaded dataset file:** 26.4 MB

### Data Fields
#### boolq_robustness
- `id` (integer): original question grouping ID
- `question` (string): variant of question from BoolQ.
- `variant_id` (integer): identifier of the variant. 0 indicates it is the original unperturbed question.
- `variant_type` (string): name of the expansion variant type. "original" is the original question; "simple" is a superficial non-semantic perturbation; "paraphrase" is a semantic paraphrase of the question.
- `possible_answers` (string): list of strings of possible answers.

### Citation Information
```
@misc{ackerman2024novelmetricmeasuringrobustness,
title={A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios},
author={Samuel Ackerman and Ella Rabinovich and Eitan Farchi and Ateret Anaby-Tavor},
year={2024},
eprint={2408.01963},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2408.01963},
}
```

# “PopQA-鲁棒性”数据集卡片

### 数据集概述
PopQA-鲁棒性（原文此处为PopQS-robustness，疑似笔误，与后续数据集实例名`popqa_robustness`一致）是PopQA数据集（https://aclanthology.org/2023.acl-long.546/）的扩展版本，针对原始输入问句添加了各类扰动变换。本数据集旨在作为基准测试集，用于评估大语言模型（Large Language Model, LLM）在应对此类问句扰动时的问答鲁棒性。

### 数据实例
#### popqa_robustness
- **数据集文件下载大小：** 26.4 MB

### 数据字段
#### boolq_robustness
- `id`（整数型）：原始问句分组标识符
- `question`（字符串型）：源自BoolQ的问句变体
- `variant_id`（整数型）：变体标识符，取值为0时代表未受扰动的原始问句
- `variant_type`（字符串型）：扩展变体类型名称，其中“original”表示原始问句；“simple”表示表层非语义扰动；“paraphrase”表示问句的语义复述变体
- `possible_answers`（字符串型）：候选答案字符串列表

### 引用信息

@misc{ackerman2024novelmetricmeasuringrobustness,
title={非对抗场景下大语言模型鲁棒性评测的新型度量指标},
author={塞缪尔·阿克曼、埃拉·拉宾诺维奇、埃坦·法尔奇、阿泰雷特·阿纳比-塔沃尔},
year={2024},
eprint={2408.01963},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2408.01963},
}