通过合并时间标识符模型，精准处理带时间限制的查询。
Temporal Information Retrieval via Time-Specifier Model Merging
- 为不同时间表述训练专用检索器并融合成统一模型。
- 在带时间约束的查询上性能提升显著，非时间查询不降反升。
- 适合需要高精度时间检索的应用场景，如历史数据查询。
数字信息的快速扩展使信息检索（IR）愈发重要。尽管密集检索方法在通用查询上已大幅提升语义匹配能力，但在包含明确时间约束（如“2015年”）的查询上表现持续不佳。现有时间信息检索（TIR）方法虽改善了时间推理，但常导致灾难性遗忘，降低非时间查询的性能。为此，我们提出时间标识符模型融合（TSM），一种新方法，在增强时间检索的同时保持非时间查询的准确性。TSM为每个时间标识符训练专用检索器，并将其融合为统一模型，实现对时间约束的精确处理而不损害非时间检索性能。在时间和非时间数据集上的大量实验表明，TSM在时间约束查询上显著提升性能，同时在非时间查询上保持优异结果，始终优于其他基线方法。代码已开源：https://github.com/seungyoonee/TSM。
原文摘要 · Abstract (English)
The rapid expansion of digital information and knowledge across structured and unstructured sources has heightened the importance of Information Retrieval (IR). While dense retrieval methods have substantially improved semantic matching for general queries, they consistently underperform on queries with explicit temporal constraints--often those containing numerical expressions and time specifiers such as ``in 2015.'' Existing approaches to Temporal Information Retrieval (TIR) improve temporal reasoning but often suffer from catastrophic forgetting, leading to reduced performance on non-temporal queries. To address this, we propose Time-Specifier Model Merging (TSM), a novel method that enhances temporal retrieval while preserving accuracy on non-temporal queries. TSM trains specialized retrievers for individual time specifiers and merges them in to a unified model, enabling precise handling of temporal constraints without compromising non-temporal retrieval. Extensive experiments on both temporal and non-temporal datasets demonstrate that TSM significantly improves performance on temporally constrained queries while maintaining strong results on non-temporal queries, consistently outperforming other baseline methods. Our code is available at https://github.com/seungyoonee/TSM .
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