mlsanityData to 14 Oct 2026

A Question Answering Dataset for Temporal-Sensitive Retrieval-Augmented Generation

arXiv 2508.12282 ↗First posted 17 Aug 2025cs.CL cs.IRcited by 0

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

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Abstract (arXiv)

We introduce ChronoQA, a large-scale benchmark dataset for Chinese question answering, specifically designed to evaluate temporal reasoning in Retrieval-Augmented Generation (RAG) systems. ChronoQA is constructed from over 300,000 news articles published between 2019 and 2024, and contains 5,176 high-quality questions covering absolute, aggregate, and relative temporal types with both explicit and implicit time expressions. The dataset supports both single- and multi-document scenarios, reflecting the real-world requirements for temporal alignment and logical consistency. ChronoQA features comprehensive structural annotations and has undergone multi-stage validation, including rule-based, LLM-based, and human evaluation, to ensure data quality. By providing a dynamic, reliable, and scalable resource, ChronoQA enables structured evaluation across a wide range of temporal tasks, and serves as a robust benchmark for advancing time-sensitive retrieval-augmented question answering systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 15 of its 27 references that are in the map. First posted on arXiv 17 Aug 2025.

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15 of 27 references.

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  1. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec
  2. 2023Temporal Knowledge Question Answering via Abstract Reasoning Induction15 Nov

Other references · 13

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  4. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  5. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,031
  6. 2024Benchmarking Retrieval-Augmented Generation for Medicinecited by 331
  7. 2023FreshLLMs: Refreshing Large Language Models with Search Engine Augmentationcited by 318
  8. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 257
  9. 2023Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?cited by 168
  10. 2024CRAG -- Comprehensive RAG Benchmarkcited by 97
  11. 2024Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmarkcited by 22
  12. 2024MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answeringcited by 17
1 more
  1. 2024An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platformscited by 3

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  1. 2024ComplexTempQA:A 100m Dataset for Complex Temporal Question Answering
  2. 2022A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases
  3. 2024UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization
  4. 2025It's High Time: A Survey of Temporal Question Answering
  5. 2025Reading Between the Timelines: RAG for Answering Diachronic Questions
  6. 2023TempTabQA: Temporal Question Answering for Semi-Structured Tables
  7. 2022Targeted Extraction of Temporal Facts from Textual Resources for Improved Temporal Question Answering over Knowledge Bases
  8. 2024LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering