Large Language Models Can Learn Temporal Reasoning
Open accessAbstract
While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they are not without their flaws and inaccuracies.Recent studies have introduced various methods to mitigate these limitations.Temporal reasoning (TR), in particular, presents a significant challenge for LLMs due to its reliance on diverse temporal concepts and intricate temporal logic.In this paper, we propose TG-LLM, a novel framework towards languagebased TR.Instead of reasoning over the original context, we adopt a latent representation, temporal graph (TG) that enhances the learning of TR.A synthetic dataset (TGQA), which is fully controllable and requires minimal supervision, is constructed for fine-tuning LLMs on this text-to-TG translation task.We confirmed in experiments that the capability of TG translation learned on our dataset can be transferred to other TR tasks and benchmarks.On top of that, we teach LLM to perform deliberate reasoning over the TGs via Chain-of-Thought (CoT) bootstrapping and graph data augmentation.We observed that those strategies, which maintain a balance between usefulness and diversity, bring more reliable CoTs and final results than the vanilla CoT distillation. 1 * Equal contribution. 1 Code and data are available at https://github.com/ xiongsiheng/TG-LLM.Once upon a time in the quaint town of Weston, a baby boy named John Thompson was brought into the world in the year 1921.Growing up, he had a vibrant spirit and an adventurous soul.…Step 1: Text-to-Temporal Graph Translation True or false: event (John Thompson owned Pearl Network) was longer in duration than event (Sophia Parker was married to John Thompson) ?The duration for each event can be calculated as follows:(John Thompson owned Pearl Network) starts at 1942, ends at 1967
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Research works printed but not linked: 7
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- Authors
- Xiong, S., Payani, A., Kompella, R., & Fekri, F.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 10452-10470
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402684293
- Cited by
- 47
- References
- 0
- Citations by year
- 2026: 18 · 2025: 18 · 2024: 11
- FWCI
- 13.87
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Cisco Systems
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- temporal reasoning, large language models, dynamic graphs, synthetic dataset, graph augmentation, temporal logic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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