README.md

---
license: mit
task_categories:
- question-answering
language:
- en
size_categories:
- 10K<n<100K
---

**Dataset Card for AdvisorQA**

As the integration of large language models into daily life is on the rise, there is still a lack of dataset for \textit{advising on subjective and personal dilemmas}. To address this gap, we introduce AdvisorQA, which aims to improve LLMs' capability to offer advice for deeply subjective concerns, utilizing the LifeProTips Reddit forum. This forum features a dynamic interaction where users post advice-seeking questions, receiving an average of 8.9 advice per query, with 164.2 upvotes from hundreds of users, embodying a \textit{collective intelligence}. Therefore, we've completed a dataset encompassing daily life questions, diverse corresponding responses, and majority vote ranking, which we use to train a helpfulness metric. In baseline experiments, models aligned with AdvisorQA dataset demonstrated improved helpfulness through our automatic metric, as well as GPT-4 and human evaluations. 
Additionally, we expanded the independent evaluation axis to include harmlessness. AdvisorQA marks a significant leap in enhancing QA systems to provide subjective, helpful, and harmless advice, showcasing LLMs' improved understanding of human subjectivity.

**Structure of Instances in AdvisorQA Dataset**
```
prefix: Advice-seeking Question
suffix: **List** of Answer Advice for each Question (prefix also is a form of the list but duplicated for efficient coding)
sft_index: The response used for SFT post-training in the list.
reward: Upvotes score of each advice in the list(=answer=response)
label: 'safe' means those QAs are safe. 'unsafe' means those QAs are not safe. (These labels are automated labelled from LPT/ULPT forums.)
```
### Dataset Sources

- **Paper:** [arxiv](https://arxiv.org/abs/2311.09585v2)
- **Code:** [code](https://github.com/minbeomkim/AdvisorQA)

**BibTeX:**
```
@article{kim2024advisorqa,
  title={AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence},
  author={Kim, Minbeom and Lee, Hwanhee and Park, Joonsuk and Lee, Hwaran and Jung, Kyomin},
  journal={arXiv preprint arXiv:2404.11826},
  year={2024}
}
```