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    <abstract>Event causality identification (ECI) is a challenging task that involves predicting causal relationships between events in text. Existing prompt-learning-based methods typically concatenate in-context examples only at the input layer, this shallow integration limits the model’s ability to capture the abstract semantic cues necessary for identifying complex causal relationships. To address this limitation, we propose a novel model called Deep In-Context Prompt (DICP), which injects in-context examples into the deeper layer of a pre-trained language model (PLM). This strategy enables the model to leverage the hierarchical semantic representations formed in deeper layers, thereby enhancing its capacity to learn high-level causal abstractions. Moreover, DICP introduces a multi-layer prompt injection mechanism, distributing diverse in-context examples across multiple transformer layers. This design allows the model to recognize a broader range of causal patterns and improves its generalization across different contexts. We evaluate the DICP model through extensive experiments on two widely used datasets, demonstrating its significant improvement in ECI performance compared to existing approaches. Furthermore, we explore the impact of varying the number of deep layers on performance, providing valuable insights into the optimal layer configuration for ECI tasks.</abstract>
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%0 Conference Proceedings
%T DICP: Deep In-Context Prompt for Event Causality Identification
%A Mu, Lin
%A Shen, Jun
%A Ni, Li
%A Sang, Lei
%A Wu, Zhize
%A Jin, Peiquan
%A Zhang, Yiwen
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F mu-etal-2025-dicp
%X Event causality identification (ECI) is a challenging task that involves predicting causal relationships between events in text. Existing prompt-learning-based methods typically concatenate in-context examples only at the input layer, this shallow integration limits the model’s ability to capture the abstract semantic cues necessary for identifying complex causal relationships. To address this limitation, we propose a novel model called Deep In-Context Prompt (DICP), which injects in-context examples into the deeper layer of a pre-trained language model (PLM). This strategy enables the model to leverage the hierarchical semantic representations formed in deeper layers, thereby enhancing its capacity to learn high-level causal abstractions. Moreover, DICP introduces a multi-layer prompt injection mechanism, distributing diverse in-context examples across multiple transformer layers. This design allows the model to recognize a broader range of causal patterns and improves its generalization across different contexts. We evaluate the DICP model through extensive experiments on two widely used datasets, demonstrating its significant improvement in ECI performance compared to existing approaches. Furthermore, we explore the impact of varying the number of deep layers on performance, providing valuable insights into the optimal layer configuration for ECI tasks.
%R 10.18653/v1/2025.findings-emnlp.139
%U https://aclanthology.org/2025.findings-emnlp.139/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.139
%P 2589-2599

Markdown (Informal)

[DICP: Deep In-Context Prompt for Event Causality Identification](https://aclanthology.org/2025.findings-emnlp.139/) (Mu et al., Findings 2025)

• DICP: Deep In-Context Prompt for Event Causality Identification (Mu et al., Findings 2025)

ACL

• Lin Mu, Jun Shen, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, and Yiwen Zhang. 2025. DICP: Deep In-Context Prompt for Event Causality Identification. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 2589–2599, Suzhou, China. Association for Computational Linguistics.