具有推理路径细化的蒙特卡洛树搜索用于对话文本到NoSQL
Monte Carlo Tree Search with Reasoning Path Refinement for Small Language Models in Conversational Text-to-NoSQL
AI总结：
本文提出Stage-MCTS框架，通过蒙特卡洛树搜索和推理路径细化，提升小型语言模型在对话文本到NoSQL任务中的查询生成能力，实验显示其EVM准确性提升达7.93%。
摘要：
NoSQL数据库因其灵活性和可扩展性，在大数据分析、地理空间应用和医疗服务中得到了广泛应用。然而，查询NoSQL数据库需要专门的技术知识，这给用户带来了较高的门槛。尽管最近的研究已经探索了文本到NoSQL问题，但它们主要集中在单轮交互上，忽略了现实世界查询的对话性质。为弥合这一差距，我们引入了对话文本到NoSQL任务，该任务根据自然语言问题、NoSQL数据库和对话历史生成NoSQL查询。为解决这一任务，我们提出了Stage-MCTS框架，通过将查询生成作为搜索问题来赋予小型语言模型（SLMs）NoSQL特定的推理能力。该框架采用由规则基于奖励引导的蒙特卡洛树搜索（MCTS）生成逐步推理数据，随后进行逐步监督微调（SFT）和自训练策略。我们进一步构建了CoNoSQL，一个包含超过2000个对话和150个数据库的跨领域数据集，以支持评估。实验表明，我们的方法优于最先进的大推理模型，将执行价值匹配（EVM）准确性提高了最高7.93%。
英文摘要：
NoSQL databases have been widely adopted in big data analytics, geospatial applications, and healthcare services, due to their flexibility and scalability. However, querying NoSQL databases requires specialized technical expertise, creating a high barrier for users. While recent studies have explored text-to-NoSQL problem, they primarily focus on single-turn interactions, ignoring the conversational nature of real-world queries. To bridge this gap, we introduce the Conversational Text-to-NoSQL task, which generates NoSQL queries given a natural language question, a NoSQL database, and the dialogue history. To address this task, we propose Stage-MCTS, a framework that endows small language models (SLMs) with NoSQL-specific reasoning capabilities by formulating query generation as a search problem. The framework employs Monte Carlo Tree Search (MCTS) guided by a rule-based reward to produce stepwise reasoning data, followed by progressive supervised fine-tuning (SFT) and self-training strategies. We further construct CoNoSQL, a cross-domain dataset with over 2,000 dialogues and 150 databases, to support evaluation. Experiments demonstrate that our approach outperforms state-of-the-art large reasoning models, improving execution value match (EVM) accuracy by up to 7.93%.
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。
