On the Temporal Question-Answering Capabilities of Large Language Models Over Anonymized Data
On the map: LLM reasoning → Knowledge- and tool-augmented reasoning
Abstract (arXiv)
The applicability of Large Language Models (LLMs) in temporal reasoning tasks over data that is not present during training is still a field that remains to be explored. In this paper we work on this topic, focusing on structured and semi-structured anonymized data. We not only develop a direct LLM pipeline, but also compare various methodologies and conduct an in-depth analysis. We identified and examined seventeen common temporal reasoning tasks in natural language, focusing on their algorithmic components. To assess LLM performance, we created the textitReasoning and Answering Temporal Ability dataset (RATA), featuring semi-structured anonymized data to ensure reliance on reasoning rather than on prior knowledge. We compared several methodologies, involving SoTA techniques such as Tree-of-Thought, self-reflexion and code execution, tuned specifically for this scenario. Our results suggest that achieving scalable and reliable solutions requires more than just standalone LLMs, highlighting the need for integrated approaches.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 15 of its 22 references that are in the map. First posted on arXiv 10 Apr 2025.
Builds on
15 of 22 references.
Closest ancestors · 3
Same direction, newest first
- 2024Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning13 Jun
- 2024Large Language Models Can Learn Temporal Reasoning12 Jan
- 2023Temporal Knowledge Question Answering via Abstract Reasoning Induction15 Nov
Other references · 12
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
- 2024Executable Code Actions Elicit Better LLM Agentscited by 537
- 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 496
- 2018The Web as a Knowledge-base for Answering Complex Questionscited by 409
- 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoningcited by 379
- 2021Question Answering Over Temporal Knowledge Graphscited by 99
- 2024A Survey on Temporal Knowledge Graph: Representation Learning and Applicationscited by 30
- 2023Time-aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answeringcited by 27
- 2023TempTabQA: Temporal Question Answering for Semi-Structured Tablescited by 27
- 2024Temporal Knowledge Graph Question Answering: A Surveycited by 9
- 2024Self-Improvement Programming for Temporal Knowledge Graph Question Answeringcited by 5
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Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2024UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization
- 2023Unlocking Temporal Question Answering for Large Language Models with Tailor-Made Reasoning Logic
- 2024Enhancing Temporal Understanding in LLMs for Semi-structured Tables
- 2025TransientTables: Evaluating LLMs' Reasoning on Temporally Evolving Semi-structured Tables
- 2026AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models
- 2023MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models
- 2025A Study into Investigating Temporal Robustness of LLMs
- 2025TRAVELER: A Benchmark for Evaluating Temporal Reasoning across Vague, Implicit and Explicit References