Abstract
Temporal Information Retrieval (TIR) has been increasingly critical given the rise of Retrieval-Augmented Generation (RAG). Since temporally mismatched evidence can be highly misleading, TIR aims to retrieve documents that are both semantically and temporally relevant to a query. Two TIR paradigms have emerged - temporal retrievers and temporal re-rankers - differing in how temporal relevance is modeled. While these paradigms provide complementary strengths, our analysis reveals that each alone falls short of robust TIR: temporal retrievers provide flexible query understanding via learned representations, but often fail to explicitly account for temporal constraints; temporal re-rankers can enforce such constraints more explicitly, but often rely on predefined re-ranking rules. To address this, we propose Q-TIE, a re-ranking framework based on learned Temporal Intent Extraction (TIE). By introducing a TIE model that maps each query's temporal constraint into a unified interval representation (i.e., $\langle t_{start}, t_{end} \rangle$), Q-TIE generalizes beyond predefined rules via model-based learning while explicitly modeling temporal constraints as a separate signal - jointly achieving what each paradigm typically trades off. Experiments demonstrate that Q-TIE consistently outperforms existing TIR methods with stronger generalizability across temporal query types, and provides a lightweight yet effective add-on for temporally-aware RAG pipelines. Code: https://github.com/ssoy0701/Q-TIE.
Keywords
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Publication details
- Journal
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- Open access
- Green open access
Cite this article
APA 7
Kim, S., Kim, H., Bak, J., & Whang, S. E. (2026). Q-TIE: A Lightweight and Generalizable Re-ranking Framework for Temporal Information Retrieval. https://omanscience.com/en/articles/q-tie-a-lightweight-and-generalizable-re-ranking-framework-for-temporal-information-retrieval
MLA 9
Kim, Soyeon, et al. "Q-TIE: A Lightweight and Generalizable Re-ranking Framework for Temporal Information Retrieval." https://omanscience.com/en/articles/q-tie-a-lightweight-and-generalizable-re-ranking-framework-for-temporal-information-retrieval.
Chicago (author–date)
Kim, Soyeon, Hyunjin Kim, JinYeong Bak, and Steven Euijong Whang. 2026. "Q-TIE: A Lightweight and Generalizable Re-ranking Framework for Temporal Information Retrieval." https://omanscience.com/en/articles/q-tie-a-lightweight-and-generalizable-re-ranking-framework-for-temporal-information-retrieval.
Harvard
Kim, S., Kim, H., Bak, J. and Whang, S. E. (2026) 'Q-TIE: A Lightweight and Generalizable Re-ranking Framework for Temporal Information Retrieval', Available at: https://omanscience.com/en/articles/q-tie-a-lightweight-and-generalizable-re-ranking-framework-for-temporal-information-retrieval.
Vancouver
Kim S, Kim H, Bak J, Whang SE. Q-TIE: A Lightweight and Generalizable Re-ranking Framework for Temporal Information Retrieval. https://omanscience.com/en/articles/q-tie-a-lightweight-and-generalizable-re-ranking-framework-for-temporal-information-retrieval
IEEE
S. Kim, H. Kim, J. Bak, and S. E. Whang, "Q-TIE: A Lightweight and Generalizable Re-ranking Framework for Temporal Information Retrieval," https://omanscience.com/en/articles/q-tie-a-lightweight-and-generalizable-re-ranking-framework-for-temporal-information-retrieval.