Temporal Information Retrieval via Time-Specifier Model Merging

2025-07-09

AI Summary

Existing dense retrieval models underperform on temporally constrained queries (e.g., containing numerical temporal expressions like “in 2015”), while dedicated temporal retrieval approaches often degrade performance on non-temporal queries. To address this trade-off, this paper proposes a temporal identifier-aware model fusion framework. It trains multiple specialized retrievers—each targeting distinct temporal identifiers (e.g., years, seasons, relative temporal terms)—and integrates them via a parameter-efficient fusion mechanism that jointly models temporal and non-temporal semantics without catastrophic forgetting. Experiments across multiple standard benchmarks demonstrate that our method significantly improves retrieval effectiveness for time-sensitive queries (+12.3% average MRR@10), while maintaining or slightly surpassing baseline performance on general queries. This achieves a synergistic optimization of temporal awareness and generalization capability.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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 .

Problem

• Improves retrieval for queries with explicit temporal constraints
• Addresses catastrophic forgetting in temporal information retrieval
• Enhances temporal retrieval without compromising non-temporal query accuracy

Innovation

• Merges specialized retrievers for time specifiers
• Preserves accuracy on non-temporal queries
• Unifies models for precise temporal handling