---
title: "UnSeenTimeQA — paper by Uddin et al. (2025)"
description: "They then introduce UnSeenTimeQA, a contamination-free benchmark of synthetic event scenarios… 34 references, cited by 10 papers here."
authors: ["Md Nayem Uddin", "Amir Saeidi", "Divij Handa", "Agastya Seth", "Tran Cao Son", "Eduardo Blanco", "Steven Corman", "Chitta Baral"]
year: 2025
venue: "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.acl-long.94/"
doi: "10.18653/v1/2025.acl-long.94"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/uddin-2025-unseentimeqa-time-sensitive-question-answering/"
---
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# UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization

**Authors:** [Md Nayem Uddin](https://md.research.elamur.ai/free/en/authors/md-nayem-uddin/), [Amir Saeidi](https://md.research.elamur.ai/free/en/authors/amir-saeidi/), [Divij Handa](https://md.research.elamur.ai/free/en/authors/divij-handa/), Agastya Seth, Tran Cao Son, [Eduardo Blanco](https://md.research.elamur.ai/free/en/authors/eduardo-blanco/), Steven Corman and [Chitta Baral](https://md.research.elamur.ai/free/en/authors/chitta-baral/) · **Year:** 2025 · **Venue:** [Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.acl-long.94/) · [PDF](https://aclanthology.org/2025.acl-long.94.pdf) · [DOI 10.18653/v1/2025.acl-long.94](https://doi.org/10.18653/v1/2025.acl-long.94) · [OpenAlex](https://openalex.org/W4412886742)

**Citations:** 4 citations (OpenAlex, 2026-10-14) · [10](https://md.research.elamur.ai/free/en/papers/uddin-2025-unseentimeqa-time-sensitive-question-answering/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Benchmarks for time-sensitive question answering (TSQA), such as TimeQA, TempReason and MenatQA, are built from real-world Wikidata/Wikipedia facts. LLMs may therefore answer from memorized pre-training data rather than by reasoning about time. Benchmarks collected after a model's knowledge cut-off avoid this only while someone keeps updating them by hand. The authors first show that models answer many questions from older benchmarks even without any context, which points to memorization. They then introduce UnSeenTimeQA, a contamination-free benchmark of synthetic event scenarios modelled on the logistics domain of the International Planning Competitions: packages are loaded, moved by truck or airplane, and unloaded over given time spans. An open-source generator can create fresh samples at any time, so the benchmark never goes stale. It covers both sequential and concurrent events. Five LLMs do well on easier subsets but worse overall than on real-world TSQA. Error analysis shows they struggle with long-range event dependencies and with parallel events. The resource offers a cleaner way to measure genuine temporal reasoning.

## Key points
- Memorization probe: 1,500 questions sampled from six existing TSQA benchmarks. Models score well even without context, for example 44% on TimeQA Easy with no context versus 74% with gold context.
- Three question types, illustrated by examples in the paper:
  - questions about a fixed time point,
  - questions about a time relative to another (before/after),
  - hypothetical questions where an event is delayed.
- The benchmark has easy, medium, hard serial and hard parallel splits.
- Error analysis of GPT-4o, the best-performing model, on 60 hard-split errors:
  - **Hard serial:** 60% of errors come from missing events in the reasoning chain, 25% from incomplete chains, and 15% from hallucination.
  - **Hard parallel:** 70% of errors come from treating concurrent events as sequential.
- Code and data are released on GitHub and on Hugging Face (`nurakib/UnSeenTimeQA`).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Md Nayem Uddin, Amir Saeidi, Divij Handa, Agastya Seth, Tran Cao Son, Eduardo Blanco, Steven Corman and Chitta Baral. 2025. UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{uddin2025unseentimeqa,
  title = {{UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorization}},
  author = {Md Nayem Uddin, Amir Saeidi, Divij Handa, Agastya Seth, Tran Cao Son, Eduardo Blanco, Steven Corman and Chitta Baral},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.94},
  url = {https://doi.org/10.18653/v1/2025.acl-long.94}
}
```

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*Updated 31 October 2026*