TEMPS: Temporal Sentence Embeddings for Temporal Information Retrieval
Quick summary
arXiv:2609.28048v1 Announce Type: cross Abstract: Modern information retrieval (IR) systems rarely represent time, yet many information needs depend on it: in clinical, journalistic, and legal search, when an event occurred can decide whether a document is relevant. Dense retrievers and Retrieval-Augmented Generation (RAG) pipelines match queries to documents well on topic but poorly on time, so they surface content that is on-topic yet temporally wrong. We introduce Temporal Textual Similarity (TTS), a task that measures how well two anchored texts align in time, independent of their topical
Key takeaways
- arXiv:2609.28048v1 Announce Type: cross Abstract: Modern information retrieval (IR) systems rarely represent time, yet many information needs depend on it: in clinical, journalistic, and legal search, when an event occurred can decide whether a document is relevant.
- Dense retrievers and Retrieval-Augmented Generation (RAG) pipelines match queries to documents well on topic but poorly on time, so they surface content that is on-topic yet temporally wrong.
- We introduce Temporal Textual Similarity (TTS), a task that measures how well two anchored texts align in time, independent of their topical
Why it matters
“TEMPS: Temporal Sentence Embeddings for Temporal Information Retrieval” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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