mlsanityData to 7 Oct 2026

DateLogicQA: Benchmarking Temporal Biases in Large Language Models

arXiv 2412.13377 ↗First posted 17 Dec 2024cs.CL cs.AIcited by 2

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Abstract (arXiv)

This paper introduces DateLogicQA, a benchmark with 190 questions covering diverse date formats, temporal contexts, and reasoning types. We propose the Semantic Integrity Metric to assess tokenization quality and analyse two biases: Representation-Level Bias, affecting embeddings, and Logical-Level Bias, influencing reasoning outputs. Our findings provide a comprehensive evaluation of LLMs' capabilities and limitations in temporal reasoning, highlighting key challenges in handling temporal data accurately.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 27 references that are in the map. First posted on arXiv 17 Dec 2024.

Builds on

20 of 27 references.

References · 20

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  4. 2024OLMo: Accelerating the Science of Language Modelscited by 599
  5. 2024Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Researchcited by 475
  6. 2024OLMoE: Open Mixture-of-Experts Language Modelscited by 291
  7. 2023Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Modelscited by 186
  8. 2024Tokenization counts: the impact of tokenization on arithmetic in frontier LLMscited by 112
  9. 2024Large Language Models Can Learn Temporal Reasoningcited by 110
  10. 2024Tokenization Is More Than Compressioncited by 87
  11. 2024Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoningcited by 83
  12. 2024Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performancecited by 61
8 more
  1. 2024How Much Can RAG Help the Reasoning of LLM?cited by 31
  2. 2024Counting Ability of Large Language Models and Impact of Tokenizationcited by 29
  3. 2024Toward a Theory of Tokenization in LLMscited by 28
  4. 2024Timo: Towards Better Temporal Reasoning for Language Modelscited by 21
  5. 2024The Foundations of Tokenization: Statistical and Computational Concernscited by 20
  6. 2023Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuningcited by 18
  7. 2024Interleaving Text and Number Embeddings to Solve Mathemathics Problemscited by 5
  8. 2024Unveiling Divergent Inductive Biases of LLMs on Temporal Datacited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Evaluating Modern Large Language Models on Low-Resource and Morphologically Rich Languages:A Cross-Lingual Benchmark Across Cantonese, Japanese, and Turkishcited by 2
  2. 2026On the Cultural Anachronism and Temporal Reasoning in Vision Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Date Fragments: A Hidden Bottleneck of Tokenization for Temporal Reasoning
  2. 2026What Really Controls Temporal Reasoning in Large Language Models: Tokenisation or Representation of Time?
  3. 2025SPAN: Benchmarking and Improving Cross-Calendar Temporal Reasoning of Large Language Models
  4. 2025TRAVELER: A Benchmark for Evaluating Temporal Reasoning across Vague, Implicit and Explicit References
  5. 2026Measuring Iterative Temporal Reasoning with Time Puzzles
  6. 2025TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in Real-World Scenarios
  7. 2023Unlocking Temporal Question Answering for Large Language Models with Tailor-Made Reasoning Logic
  8. 2023TRAM: Benchmarking Temporal Reasoning for Large Language Models