@inproceedings{hao-etal-2026-sere,
title = "{SERE}: Structural Example Retrieval for Enhancing {LLM}s in Event Causality Identification",
author = "Hao, Zhifeng and
Chen, Zhongjie and
Lu, Junhao and
Yu, Shengyin and
Hu, Guimin and
Zhang, Keli and
Cai, Ruichu and
Xu, Boyan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2126/",
pages = "42861--42886",
ISBN = "979-8-89176-395-1",
abstract = "Event Causality Identification (ECI) requires models to determine whether a given pair of events in a context exhibits a causal relationship. While Large Language Models (LLMs) have demonstrated strong performance across various NLP tasks, their effectiveness in ECI remains limited due to biases in causal reasoning, often leading to overprediction of causal relationships (causal hallucination). To mitigate these issues and enhance LLM performance in ECI, we propose \textbf{SERE}, a structural example retrieval framework that leverages LLMs' few-shot learning capabilities. \textbf{SERE} introduces an innovative retrieval mechanism based on three structural concepts: (i) \textbf{Conceptual Path Metric}, which measures the conceptual relationship between events using edit distance in ConceptNet; (ii) \textbf{Syntactic Metric}, which quantifies structural similarity through tree edit distance on syntactic trees; and (iii) \textbf{Causal Pattern Filtering}, which filters examples based on predefined causal structures using LLMs. By integrating these structural retrieval strategies, \textbf{SERE} selects more relevant examples to guide LLMs in causal reasoning, mitigating bias and improving accuracy in ECI tasks. Extensive experiments on multiple ECI datasets validate the effectiveness of \textbf{SERE}."
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<abstract>Event Causality Identification (ECI) requires models to determine whether a given pair of events in a context exhibits a causal relationship. While Large Language Models (LLMs) have demonstrated strong performance across various NLP tasks, their effectiveness in ECI remains limited due to biases in causal reasoning, often leading to overprediction of causal relationships (causal hallucination). To mitigate these issues and enhance LLM performance in ECI, we propose SERE, a structural example retrieval framework that leverages LLMs’ few-shot learning capabilities. SERE introduces an innovative retrieval mechanism based on three structural concepts: (i) Conceptual Path Metric, which measures the conceptual relationship between events using edit distance in ConceptNet; (ii) Syntactic Metric, which quantifies structural similarity through tree edit distance on syntactic trees; and (iii) Causal Pattern Filtering, which filters examples based on predefined causal structures using LLMs. By integrating these structural retrieval strategies, SERE selects more relevant examples to guide LLMs in causal reasoning, mitigating bias and improving accuracy in ECI tasks. Extensive experiments on multiple ECI datasets validate the effectiveness of SERE.</abstract>
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%0 Conference Proceedings
%T SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification
%A Hao, Zhifeng
%A Chen, Zhongjie
%A Lu, Junhao
%A Yu, Shengyin
%A Hu, Guimin
%A Zhang, Keli
%A Cai, Ruichu
%A Xu, Boyan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F hao-etal-2026-sere
%X Event Causality Identification (ECI) requires models to determine whether a given pair of events in a context exhibits a causal relationship. While Large Language Models (LLMs) have demonstrated strong performance across various NLP tasks, their effectiveness in ECI remains limited due to biases in causal reasoning, often leading to overprediction of causal relationships (causal hallucination). To mitigate these issues and enhance LLM performance in ECI, we propose SERE, a structural example retrieval framework that leverages LLMs’ few-shot learning capabilities. SERE introduces an innovative retrieval mechanism based on three structural concepts: (i) Conceptual Path Metric, which measures the conceptual relationship between events using edit distance in ConceptNet; (ii) Syntactic Metric, which quantifies structural similarity through tree edit distance on syntactic trees; and (iii) Causal Pattern Filtering, which filters examples based on predefined causal structures using LLMs. By integrating these structural retrieval strategies, SERE selects more relevant examples to guide LLMs in causal reasoning, mitigating bias and improving accuracy in ECI tasks. Extensive experiments on multiple ECI datasets validate the effectiveness of SERE.
%U https://aclanthology.org/2026.findings-acl.2126/
%P 42861-42886
Markdown (Informal)
[SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification](https://aclanthology.org/2026.findings-acl.2126/) (Hao et al., Findings 2026)
ACL
- Zhifeng Hao, Zhongjie Chen, Junhao Lu, Shengyin Yu, Guimin Hu, Keli Zhang, Ruichu Cai, and Boyan Xu. 2026. SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification. In Findings of the Association for Computational Linguistics: ACL 2026, pages 42861–42886, San Diego, California, United States. Association for Computational Linguistics.