ChunQiuTR: Time-Keyed Temporal Retrieval in Classical Chinese Annals

arXiv cs.CL / 4/9/2026

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Key Points

  • ChunQiuTR is introduced as a new benchmark for retrieval-augmented generation in historical research, focusing on “time-keyed” access where the correct regnal month matters as much as topical relevance.
  • The paper highlights the difficulty of Classical Chinese annals, where temporal expressions are implicit and non-Gregorian, making it easy for retrieval systems to return semantically plausible but chronologically incorrect evidence.
  • ChunQiuTR is built from the Spring and Autumn Annals and its exegetical tradition, organized by month-level reign keys and paired with chrono-near confounders to simulate realistic temporal retrieval failures.
  • A new model, CTD (Calendrical Temporal Dual-encoder), is proposed to improve temporal consistency by combining Fourier-based absolute calendrical context with relative offset biasing.
  • Experiments report consistent improvements over semantic dual-encoder baselines on time-keyed evaluation, arguing that temporal consistency is a prerequisite for faithful historical RAG.

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 \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}}.