Reading Between the Timelines: RAG for Answering Diachronic Questions

• URL: http://arxiv.org/abs/2507.22917v1
• Date: Mon, 21 Jul 2025 05:19:41 GMT
• Title: Reading Between the Timelines: RAG for Answering Diachronic Questions
• Authors: Kwun Hang Lau, Ruiyuan Zhang, Weijie Shi, Xiaofang Zhou, Xiaojun Cheng,
• Abstract summary: We propose a new framework that fundamentally redesigns the RAG pipeline to infuse temporal logic.<n>Our approach yields substantial gains in answer accuracy, surpassing standard RAG implementations by 13% to 27%.<n>This work provides a validated pathway toward RAG systems capable of performing the nuanced, evolutionary analysis required for complex, real-world questions.
• Score: 8.969698902720799
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: While Retrieval-Augmented Generation (RAG) excels at injecting static, factual knowledge into Large Language Models (LLMs), it exhibits a critical deficit in handling longitudinal queries that require tracking entities and phenomena across time. This blind spot arises because conventional, semantically-driven retrieval methods are not equipped to gather evidence that is both topically relevant and temporally coherent for a specified duration. We address this challenge by proposing a new framework that fundamentally redesigns the RAG pipeline to infuse temporal logic. Our methodology begins by disentangling a user's query into its core subject and its temporal window. It then employs a specialized retriever that calibrates semantic matching against temporal relevance, ensuring the collection of a contiguous evidence set that spans the entire queried period. To enable rigorous evaluation of this capability, we also introduce the Analytical Diachronic Question Answering Benchmark (ADQAB), a challenging evaluation suite grounded in a hybrid corpus of real and synthetic financial news. Empirical results on ADQAB show that our approach yields substantial gains in answer accuracy, surpassing standard RAG implementations by 13% to 27%. This work provides a validated pathway toward RAG systems capable of performing the nuanced, evolutionary analysis required for complex, real-world questions. The dataset and code for this study are publicly available at https://github.com/kwunhang/TA-RAG.

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