ReaGeo：基于大语言模型的推理增强端到端地名编码
ReaGeo: Reasoning-Enhanced End-to-End Geocoding with LLMs
AI总结：
ReaGeo利用大语言模型解决传统多阶段方法在地理数据库中依赖文本或向量相似性检索的局限性，通过将坐标转换为geohash序列，引入链式推理机制提升空间关系推理能力，并通过距离偏差奖励的强化学习优化生成精度。
摘要：
本文提出ReaGeo，一种基于大语言模型的端到端地名编码框架，旨在克服传统多阶段方法在地理数据库中依赖文本或向量相似性检索的局限性，包括工作流程复杂、误差传播和对结构化地理知识库的依赖。该方法将地理坐标转换为geohash序列，将坐标预测任务重新表述为文本生成问题，并引入链式推理机制以增强模型对空间关系的推理能力。此外，应用基于距离偏差的强化学习来优化生成精度。全面的实验表明，ReaGeo能够准确处理单点预测的显式地址查询，并有效解决模糊的相对位置查询。此外，该模型在非点几何区域的预测能力也表现出色，突显了其在地名编码任务中的通用性和适应性。
英文摘要：
This paper proposes ReaGeo, an end-to-end geocoding framework based on large language models, designed to overcome the limitations of traditional multi-stage approaches that rely on text or vector similarity retrieval over geographic databases, including workflow complexity, error propagation, and heavy dependence on structured geographic knowledge bases. The method converts geographic coordinates into geohash sequences, reformulating the coordinate prediction task as a text generation problem, and introduces a Chain-of-Thought mechanism to enhance the model's reasoning over spatial relationships. Furthermore, reinforcement learning with a distance-deviation-based reward is applied to optimize the generation accuracy. Comprehensive experiments show that ReaGeo can accurately handle explicit address queries in single-point predictions and effectively resolve vague relative location queries. In addition, the model demonstrates strong predictive capability for non-point geometric regions, highlighting its versatility and generalization ability in geocoding tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
