ChunQiuTR: Time-Keyed Temporal Retrieval in Classical Chinese Annals
This addresses the challenge of ensuring temporal consistency in retrieval-augmented generation for historical research, particularly in Classical Chinese studies, though it is incremental as it builds on existing retrieval methods.
The paper tackles the problem of retrieving temporally consistent records for specific regnal months in Classical Chinese annals, where time is expressed implicitly, and introduces ChunQiuTR, a time-keyed retrieval benchmark, and CTD, a time-aware dual-encoder method, achieving consistent gains over semantic baselines in time-keyed evaluation.
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 \textbf{ChunQiuTR}, a time-keyed retrieval benchmark built from the \textit{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 \textbf{CTD} (Calendrical Temporal Dual-encoder), a time-aware dual-encoder that combines Fourier-based 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 \href{https://github.com/xbdxwyh/ChunQiuTR}{\texttt{github.com/xbdxwyh/ChunQiuTR}}.