Year: 2026
Type: conference-paper
Abstract: Retrieval shapes how language models access and ground knowledge in retrieval-augmented generation (RAG).In historical research, the target is often not an arbitrary relevant passage, but the exact record for a specific regnal month, where temporal consistency matters as much as topical relevance.This is especially challenging for Classical Chinese annals, where time is expressed through terse, implicit, non-Gregorian reign phrases that must be interpreted from surrounding context, so semantically plausible evidence can still be temporally invalid.We introduce ChunQi-uTR, a time-keyed retrieval benchmark built from the Spring and Autumn Annals and its exegetical tradition.ChunQiuTR organizes records by month-level reign keys and includes chrono-near confounders that mirror realistic retrieval failures.We further propose CTD (Calendrical Temporal Dual-encoder), a timeaware dual-encoder that combines Fourierbased absolute calendrical context with relative offset biasing.Experiments show consistent gains over strong semantic dual-encoder baselines under time-keyed evaluation, supporting retrieval-time temporal consistency as a key prerequisite for faithful downstream historical RAG.Our code and datasets are available at github.com/xbdxwyh/ChunQiuTR.
Authors: Yihao Wang, Zijian He, Jie Ren, Keze Wang
Institutions: Sun Yat-sen University, Shaanxi Normal University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G1099008978, G327634725, G3723030115
OpenAlex ID: W7166806552