AdvisorQA: Towards Helpful and Harmless Advice-seeking Question
  Answering with Collective Intelligence
Minbeom Kim Hwanhee Lee Joonsuk Park Hwaran Lee Kyomin Jung
As the integration of large language models into daily life is on the rise,
there is a clear gap in benchmarks for advising on subjective and personal
dilemmas. To address this, we introduce AdvisorQA, the first benchmark
developed to assess LLMs' capability in offering advice for deeply personalized
concerns, utilizing the LifeProTips subreddit 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 collective intelligence framework. Therefore, we've completed a
benchmark encompassing daily life questions, diverse corresponding responses,
and majority vote ranking to train our helpfulness metric. Baseline experiments
validate the efficacy of AdvisorQA through our helpfulness metric, GPT-4, and
human evaluation, analyzing phenomena beyond the trade-off between helpfulness
and harmlessness. AdvisorQA marks a significant leap in enhancing QA systems
for providing personalized, empathetic advice, showcasing LLMs' improved
understanding of human subjectivity.