@inproceedings{kim-etal-2025-advisorqa,
title = "{A}dvisor{QA}: 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",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.333/",
pages = "6545--6565",
ISBN = "979-8-89176-189-6",
abstract = "As the integration of large language models into daily life is on the rise, there is still a lack of dataset for *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 *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."
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<abstract>As the integration of large language models into daily life is on the rise, there is still a lack of dataset for *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 *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.</abstract>
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%0 Conference Proceedings
%T AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence
%A Kim, Minbeom
%A Lee, Hwanhee
%A Park, Joonsuk
%A Lee, Hwaran
%A Jung, Kyomin
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F kim-etal-2025-advisorqa
%X As the integration of large language models into daily life is on the rise, there is still a lack of dataset for *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 *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.
%U https://aclanthology.org/2025.naacl-long.333/
%P 6545-6565
Markdown (Informal)
[AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence](https://aclanthology.org/2025.naacl-long.333/) (Kim et al., NAACL 2025)
ACL