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<p>Explainable prediction from complex data like text and time series is a critical challenge for intelligent service-oriented applications. Traditional models often lack transparency, hindering trust in service contexts where understanding data nuances is key. Dynamic pricing in online rental services like Airbnb exemplifies this, where explainable price forecasts based on factors such as guest reviews are vital for service optimization. Large Language Models (LLMs) show promise for explainability but face challenges with noisy data interpretation and costly expert annotation for fine-tuning in these service-specific tasks. To address these issues, we introduce the Guided Self-Reflection framework. Our key insight is enabling an LLM to autonomously generate high-quality “Golden data” from public service information (e.g., Airbnb listings and reviews) via a structured, iterative self-reflection process. This reflection is meticulously guided by content-specific criteria: Evidence Relevance and Faithfulness, Logical Consistency and Coherence, and Domain Commonsense Alignment (pertinent to Airbnb price dynamics). This process teaches the LLM to discern and articulate the interplay between textual feedback and price movements. The LLM agent then leverages this autonomously generated data, including positive and negative reflective examples, to self-fine-tune its predictive and explanatory capabilities using Proximal Policy Optimization (PPO), thereby eliminating the need for costly manual annotation. Applying this framework, we fine-tune a specialized LLM for explainable predictions of Airbnb rental price movements, conditioned on historical prices and contemporary guest reviews. Our methodology demonstrates potential to significantly outperform traditional deep learning techniques and other LLM applications in both predictive accuracy and the Matthews Correlation Coefficient (MCC). Critically, it offers a pathway to generating transparent and trustworthy explanations for price dynamics, enhancing the intelligence and user-centricity of service-oriented pricing applications and contributing to more robust, interpretable AI solutions in the service industry.</p>

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Explainable dynamic service price prediction using guided self-reflective large language models

  • 期刊论文

  • 发表日期：

    2026年4月15日

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  • Chi-Ze Yu,
  • Ziyu Li,
  • Yaoyao Wang,
  • Weiping Li,
  • Tong Mo

摘要

Explainable prediction from complex data like text and time series is a critical challenge for intelligent service-oriented applications. Traditional models often lack transparency, hindering trust in service contexts where understanding data nuances is key. Dynamic pricing in online rental services like Airbnb exemplifies this, where explainable price forecasts based on factors such as guest reviews are vital for service optimization. Large Language Models (LLMs) show promise for explainability but face challenges with noisy data interpretation and costly expert annotation for fine-tuning in these service-specific tasks. To address these issues, we introduce the Guided Self-Reflection framework. Our key insight is enabling an LLM to autonomously generate high-quality “Golden data” from public service information (e.g., Airbnb listings and reviews) via a structured, iterative self-reflection process. This reflection is meticulously guided by content-specific criteria: Evidence Relevance and Faithfulness, Logical Consistency and Coherence, and Domain Commonsense Alignment (pertinent to Airbnb price dynamics). This process teaches the LLM to discern and articulate the interplay between textual feedback and price movements. The LLM agent then leverages this autonomously generated data, including positive and negative reflective examples, to self-fine-tune its predictive and explanatory capabilities using Proximal Policy Optimization (PPO), thereby eliminating the need for costly manual annotation. Applying this framework, we fine-tune a specialized LLM for explainable predictions of Airbnb rental price movements, conditioned on historical prices and contemporary guest reviews. Our methodology demonstrates potential to significantly outperform traditional deep learning techniques and other LLM applications in both predictive accuracy and the Matthews Correlation Coefficient (MCC). Critically, it offers a pathway to generating transparent and trustworthy explanations for price dynamics, enhancing the intelligence and user-centricity of service-oriented pricing applications and contributing to more robust, interpretable AI solutions in the service industry.