超越趣味性：用于视觉数据分析的语义和上下文感知自然语言查询推荐
Beyond Interestingness: Semantic and Context-Aware Natural Language Query Recommendations for Visual Data Analysis
AI总结：
针对分析师分解查询目标的挑战，研究用语义和上下文感知推荐增强自然语言接口，联合整合多种因素引导数据库探索，经多方式评估，QRec-NLI在代理比较和用户研究中表现优于基线。
摘要：
大语言模型的进展使自然语言接口可广泛用于数据探索，但分析师仍面临将宽泛分析目标分解为有效逐步查询的挑战，尤其是针对不熟悉的多表关系数据库。我们研究如何用语义和上下文感知的下一步查询推荐增强自然语言接口，为关系数据库探索提供分析框架。我们的方法超越仅考虑趣味性的方法，联合整合语义相关性、数据趣味性和上下文连贯性，引导探索走向连贯、主题聚焦的分析和潜在有洞察力的子集。我们通过NL2SQL基准测试、大语言模型增强的描述验证、与仅考虑趣味性和基于大语言模型提示的基线进行代理比较以及12名参与者的用户研究来评估QRec-NLI。在代理比较中，QRec-NLI产生的主题相关和局部连贯的查询序列比两个基线都多。在与仅考虑趣味性基线的用户研究中，它在生成洞察力支持和决策支持方面获得更高评分。
英文摘要：
Recent advances in large language models (LLMs) have made natural language interfaces (NLIs) widely accessible for data exploration, yet analysts who have a broad analytical objective still face the challenge of decomposing it into effective step-by-step queries, especially over unfamiliar, multi-table relational databases. Rather than generating high-level analytical agendas, we investigate how to augment an NLI with semantic- and context-aware next-step query recommendations that act as analytical scaffolding for relational database exploration. Our approach goes beyond interestingness-only methods by jointly integrating semantic relevance, data interestingness, and context coherence to guide exploration toward coherent, topic-focused analyses and potentially insightful subsets. We evaluate QRec-NLI with NL2SQL benchmarking, LLM-enhanced description validation, agentic comparisons against interestingness-only and LLM-based prompting baselines, and a 12-participant user study. In the agentic comparison, QRec-NLI yields more topically relevant and locally coherent query sequences than both baselines. In the user study against the interestingness-only baseline, it receives stronger ratings for insight-generation support and decision support.
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。
