@inproceedings{wu-etal-2026-llms,
title = "Can {LLM}s Access their World Knowledge for Event Prediction?",
author = "Wu, Zijun and
Zhang, Zhitian and
Hajimirsadeghi, Hossein and
Dvornik, Nikita and
Pashevich, Alexander",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.823/",
doi = "10.18653/2026.findings-emnlp.823",
pages = "17442--17457",
ISBN = "979-8-89176-462-0",
abstract = "Recent work has questioned whether large language models (LLMs) can be useful for temporal event prediction. Despite vast pretrained world knowledge, LLMs often underperform simpler specialized models. We ask whether this knowledge is truly inaccessible, or whether current methods fail to present event prediction in a form LLMs can use. We argue for the latter and show that LLMs become effective event forecasters when the interface is designed around two choices. The first grounds each event in a natural-language description, and the second separates event semantics from the prediction space to prevent semantic bias from label associations. To test this, we introduce DescribeEvent, a benchmark of {\textasciitilde}150K events across seven datasets and six domains with event-level descriptions. We find that capable LLMs substantially benefit from descriptions and symbolic labels, outperforming description-free variants and non-LLM baselines, while weaker models gain little. These results suggest that accessing LLM world knowledge for event prediction is an interface problem, and that event descriptions offer a practical way to elicit the knowledge LLMs already encode."
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<abstract>Recent work has questioned whether large language models (LLMs) can be useful for temporal event prediction. Despite vast pretrained world knowledge, LLMs often underperform simpler specialized models. We ask whether this knowledge is truly inaccessible, or whether current methods fail to present event prediction in a form LLMs can use. We argue for the latter and show that LLMs become effective event forecasters when the interface is designed around two choices. The first grounds each event in a natural-language description, and the second separates event semantics from the prediction space to prevent semantic bias from label associations. To test this, we introduce DescribeEvent, a benchmark of ~150K events across seven datasets and six domains with event-level descriptions. We find that capable LLMs substantially benefit from descriptions and symbolic labels, outperforming description-free variants and non-LLM baselines, while weaker models gain little. These results suggest that accessing LLM world knowledge for event prediction is an interface problem, and that event descriptions offer a practical way to elicit the knowledge LLMs already encode.</abstract>
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%0 Conference Proceedings
%T Can LLMs Access their World Knowledge for Event Prediction?
%A Wu, Zijun
%A Zhang, Zhitian
%A Hajimirsadeghi, Hossein
%A Dvornik, Nikita
%A Pashevich, Alexander
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wu-etal-2026-llms
%X Recent work has questioned whether large language models (LLMs) can be useful for temporal event prediction. Despite vast pretrained world knowledge, LLMs often underperform simpler specialized models. We ask whether this knowledge is truly inaccessible, or whether current methods fail to present event prediction in a form LLMs can use. We argue for the latter and show that LLMs become effective event forecasters when the interface is designed around two choices. The first grounds each event in a natural-language description, and the second separates event semantics from the prediction space to prevent semantic bias from label associations. To test this, we introduce DescribeEvent, a benchmark of ~150K events across seven datasets and six domains with event-level descriptions. We find that capable LLMs substantially benefit from descriptions and symbolic labels, outperforming description-free variants and non-LLM baselines, while weaker models gain little. These results suggest that accessing LLM world knowledge for event prediction is an interface problem, and that event descriptions offer a practical way to elicit the knowledge LLMs already encode.
%R 10.18653/2026.findings-emnlp.823
%U https://aclanthology.org/2026.findings-emnlp.823/
%U https://doi.org/10.18653/2026.findings-emnlp.823
%P 17442-17457
Markdown (Informal)
[Can LLMs Access their World Knowledge for Event Prediction?](https://aclanthology.org/2026.findings-emnlp.823/) (Wu et al., Findings 2026)
ACL
- Zijun Wu, Zhitian Zhang, Hossein Hajimirsadeghi, Nikita Dvornik, and Alexander Pashevich. 2026. Can LLMs Access their World Knowledge for Event Prediction?. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 17442–17457, Budapest, Hungary. Association for Computational Linguistics.