@inproceedings{mattie-etal-2026-x,
title = "{X}-{NLP} at {S}em{E}val-2026 Task 12: Prompting {LLM}s for Abductive Event Reasoning",
author = "Mattie, Caelen and
Bowen, Patrick and
King, Milton",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.289/",
pages = "2281--2289",
ISBN = "979-8-89176-414-9",
abstract = "In this work, we applied two different systems to the SemEval 2026 Shared Task 12, which exploresabductive event reasoning. Specifically, this task involves determining the cause of an event from a list of candidate causes. Instances are accompanied with documents that can provide applicable knowledge for the target event. Both of our systems involve prompting LLMS and our best performing system leverages retrieval-augmented generation. Our best performing system achieved a score of 84{\%} and ranked 40th out of the 221 total submissions."
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<abstract>In this work, we applied two different systems to the SemEval 2026 Shared Task 12, which exploresabductive event reasoning. Specifically, this task involves determining the cause of an event from a list of candidate causes. Instances are accompanied with documents that can provide applicable knowledge for the target event. Both of our systems involve prompting LLMS and our best performing system leverages retrieval-augmented generation. Our best performing system achieved a score of 84% and ranked 40th out of the 221 total submissions.</abstract>
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%0 Conference Proceedings
%T X-NLP at SemEval-2026 Task 12: Prompting LLMs for Abductive Event Reasoning
%A Mattie, Caelen
%A Bowen, Patrick
%A King, Milton
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F mattie-etal-2026-x
%X In this work, we applied two different systems to the SemEval 2026 Shared Task 12, which exploresabductive event reasoning. Specifically, this task involves determining the cause of an event from a list of candidate causes. Instances are accompanied with documents that can provide applicable knowledge for the target event. Both of our systems involve prompting LLMS and our best performing system leverages retrieval-augmented generation. Our best performing system achieved a score of 84% and ranked 40th out of the 221 total submissions.
%U https://aclanthology.org/2026.semeval-1.289/
%P 2281-2289
Markdown (Informal)
[X-NLP at SemEval-2026 Task 12: Prompting LLMs for Abductive Event Reasoning](https://aclanthology.org/2026.semeval-1.289/) (Mattie et al., SemEval 2026)
ACL