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metadata
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

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}
}