{"ID":2857202,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10331","arxiv_id":"2510.10331","title":"LLM-Friendly Knowledge Representation for Customer Support","abstract":"We propose a practical approach by integrating Large Language Models (LLMs) with a framework designed to navigate the complexities of Airbnb customer support operations. In this paper, our methodology employs a novel reformatting technique, the Intent, Context, and Action (ICA) format, which transforms policies and workflows into a structure more comprehensible to LLMs. Additionally, we develop a synthetic data generation strategy to create training data with minimal human intervention, enabling cost-effective fine-tuning of our model. Our internal experiments (not applied to Airbnb products) demonstrate that our approach of restructuring workflows and fine-tuning LLMs with synthetic data significantly enhances their performance, setting a new benchmark for their application in customer support. Our solution is not only cost-effective but also improves customer support, as evidenced by both accuracy and manual processing time evaluation metrics.","short_abstract":"We propose a practical approach by integrating Large Language Models (LLMs) with a framework designed to navigate the complexities of Airbnb customer support operations. In this paper, our methodology employs a novel reformatting technique, the Intent, Context, and Action (ICA) format, which transforms policies and wor...","url_abs":"https://arxiv.org/abs/2510.10331","url_pdf":"https://arxiv.org/pdf/2510.10331v1","authors":"[\"Hanchen Su\",\"Wei Luo\",\"Wei Han\",\"Yu Elaine Liu\",\"Yufeng Wayne Zhang\",\"Cen Mia Zhao\",\"Ying Joy Zhang\",\"Yashar Mehdad\"]","published":"2025-10-11T20:24:50Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}