@inproceedings{shi-etal-2026-wildfeedback,
    title = "{W}ild{F}eedback: Aligning {LLM}s With In-situ User Interactions And Feedback",
    author = "Shi, Taiwei  and
      Wang, Zhuoer  and
      Yang, Longqi  and
      Lin, Ying-Chun  and
      He, Zexue  and
      Wan, Mengting  and
      Zhou, Pei  and
      Jauhar, Sujay Kumar  and
      Chen, Sihao  and
      Xia, Shan  and
      Zhang, Hongfei  and
      Zhao, Jieyu  and
      Xu, Xiaofeng  and
      Song, Xia  and
      Neville, Jennifer",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1701/",
    pages = "36701--36725",
    ISBN = "979-8-89176-390-6",
    abstract = "As large language models (LLMs) continue to advance, aligning these models with human preferences has emerged as a critical challenge. Traditional alignment methods, relying on human or LLM annotated datasets, are limited by their resource-intensive nature, inherent subjectivity, misalignment with real-world user preferences, and the risk of feedback loops that amplify model biases. To overcome these limitations, we introduce WildFeedback, a novel framework that leverages in-situ user feedback during conversations with LLMs to create preference datasets automatically. Given a corpus of multi-turn user-LLM conversation, WildFeedback identifies and classifies user feedback to LLM responses between conversation turns. The user feedback is then used to create examples of preferred and dispreferred responses according to users' preference. Our experiments demonstrate that LLMs fine-tuned on WildFeedback dataset exhibit significantly improved alignment with user preferences, as evidenced by both traditional benchmarks and our proposed checklist-guided evaluation. By incorporating in-situ feedback from actual users, WildFeedback addresses the scalability, subjectivity, and bias challenges that plague existing approaches, marking a significant step toward developing LLMs that are more responsive to the diverse and evolving needs of their users."
}