@inproceedings{zhu-etal-2025-llm-based,
    title = "{LLM}-based Conversational Recommendation Agents with Collaborative Verbalized Experience",
    author = "Zhu, Yaochen  and
      Steck, Harald  and
      Liang, Dawen  and
      He, Yinhan  and
      Kallus, Nathan  and
      Li, Jundong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.119/",
    doi = "10.18653/v1/2025.findings-emnlp.119",
    pages = "2207--2220",
    ISBN = "979-8-89176-335-7",
    abstract = "Large language models (LLMs) have demonstrated impressive zero-shot capabilities in conversational recommender systems (CRS). However, effectively utilizing historical conversations remains a significant challenge. Current approaches either retrieve few-shot examples or extract global rules to enhance the prompt, which fail to capture the implicit and preference-oriented knowledge. To address this challenge, we propose LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience, abbreviated as CRAVE. CRAVE begins by sampling trajectories of LLM-based CRS agents on historical queries and establishing verbalized experience banks by reflecting the agents' actions on user feedback. Additionally, we introduce a collaborative retriever network fine-tuned with item content-parameterized multinomial likelihood on query-item pairs to retrieve preference-oriented verbal experiences for new queries. Furthermore, we developed a debater-critic agent (DCA) system where each agent maintains an independent collaborative experience bank and works together to enhance the CRS recommendations. We demonstrate that the open-ended debate and critique nature of DCA benefits significantly from the collaborative experience augmentation with CRAVE. The code is available at https://github.com/yaochenzhu/CRAVE."
}