@inproceedings{zhu-etal-2026-kdw,
title = "{KDW} at {S}em{E}val-2026 Task 12: Logic-Driven Distillation with Knowledge Graphs for Efficient Abductive Reasoning",
author = "Zhu, Sihan and
Wu, Hongjie and
Xu, Xinyan",
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-1.124/",
pages = "905--912",
ISBN = "979-8-89176-414-9",
abstract = "Large language models (LLMs) such as GPT-4 and Gemini show strong reasoning ability but incur substantial computational cost in abductive reasoning settings. We present our system for ``SemEval-2026 Task 12 {---} Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models'', which integrates knowledge graph (KG) evidence extraction with knowledge distillation to transfer structured reasoning from a large teacher model to a compact student model. Our approach ranks 8th in the shared task while achieving performance comparable to frontier LLMs at a fraction of the inference cost."
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<abstract>Large language models (LLMs) such as GPT-4 and Gemini show strong reasoning ability but incur substantial computational cost in abductive reasoning settings. We present our system for “SemEval-2026 Task 12 — Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models”, which integrates knowledge graph (KG) evidence extraction with knowledge distillation to transfer structured reasoning from a large teacher model to a compact student model. Our approach ranks 8th in the shared task while achieving performance comparable to frontier LLMs at a fraction of the inference cost.</abstract>
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%0 Conference Proceedings
%T KDW at SemEval-2026 Task 12: Logic-Driven Distillation with Knowledge Graphs for Efficient Abductive Reasoning
%A Zhu, Sihan
%A Wu, Hongjie
%A Xu, Xinyan
%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 zhu-etal-2026-kdw
%X Large language models (LLMs) such as GPT-4 and Gemini show strong reasoning ability but incur substantial computational cost in abductive reasoning settings. We present our system for “SemEval-2026 Task 12 — Abductive Event Reasoning: Towards Real-World Event Causal Inference for Large Language Models”, which integrates knowledge graph (KG) evidence extraction with knowledge distillation to transfer structured reasoning from a large teacher model to a compact student model. Our approach ranks 8th in the shared task while achieving performance comparable to frontier LLMs at a fraction of the inference cost.
%U https://aclanthology.org/2026.semeval-1.124/
%P 905-912
Markdown (Informal)
[KDW at SemEval-2026 Task 12: Logic-Driven Distillation with Knowledge Graphs for Efficient Abductive Reasoning](https://aclanthology.org/2026.semeval-1.124/) (Zhu et al., SemEval 2026)
ACL