In Natural Language Processing(NLP), Event Temporal Relation Extraction (ETRE) is to recognize the temporal relations of two events. Prior studies have noted the importance of language models for ETRE. However, the restricted pre-trained knowledge of Small Language Models(SLMs) limits their capability to handle minority class relations in imbalanced classification datasets. For Large Language Models(LLMs), researchers adopt manually designed prompts or instructions, which may introduce extra noise, leading to interference with the model’s judgment of the long-distance dependencies between events. To address these issues, we propose GDLLM, a Global Distance-aware modeling approach based on LLMs. We first present a distance-aware graph structure utilizing Graph Attention Network(GAT) to assist the LLMs in capturing long-distance dependency features. Additionally, we design a temporal feature learning paradigm based on soft inference to augment the identification of relations with a short-distance proximity band, which supplements the probabilistic information generated by LLMs into the multi-head attention mechanism. Since the global feature can be captured effectively, our framework substantially enhances the performance of minority relation classes and improves the overall learning ability. Experiments on two publicly available datasets, TB-Dense and MATRES, demonstrate that our approach achieves state-of-the-art (SOTA) performance.

Document-level event argument extraction (EAE) is a critical task in natural language processing. While most prior approaches rely on supervised training with large labeled datasets or resource-intensive fine-tuning, recent studies explore in-context learning (ICL) with LLMs to reduce data dependence and training costs. However, the performance of ICL-based methods still lags behind fully supervised models.We highlight a key reason for this shortfall: the lack of sufficient extraction rules. In this paper, we conduct a systematic study of using hierarchical rules to enhance LLMs’ ICL capabilities. We first define three types of hierarchical rules and demonstrate their effectiveness in enhancing the performance of LLMs for document-level EAE. Building on this, we further propose an LLM-driven HiErarchical Rule Optimization (HERO) framework that iteratively generates and selects optimal hierarchical rules. Specifically, in each iteration, high-value instances are selected to produce error feedback, which is used to update and expand hierarchical rule sets. This results in multiple candidate hierarchical rule sets, from which the optimal one is selected using a scoring-based mechanism. During inference, prompts are constructed using the optimal hierarchical rules to enhance ICL performance of LLMs. Extensive experiments demonstrate the effectiveness of HERO, surpassing few-shot supervised methods and outperforming state-of-the-art prompting baselines by 3.18% F1 on RAMS, 4.30% F1 on DocEE-N, and 3.17% F1 on DocEE-C.

Understanding and analyzing event temporal relations is a crucial task in Natural Language Processing (NLP). This task, known as Event Temporal Relation Extraction (ETRE), aims to identify and extract temporal connections between events in text. Recent studies focus on locating the relative position of event pairs on the timeline by designing logical expressions or auxiliary tasks to predict their temporal occurrence. Despite these advances, this modeling approach neglects the multidimensional information in temporal relation and the hierarchical process of reasoning. In this study, we propose a novel hierarchical modeling approach for this task by introducing a Temporal Cognitive Tree (TCT) that mimics human logical reasoning. Additionally, we also design a integrated model incorporating prompt optimization and deductive reasoning to exploit multidimensional supervised information. Extensive experiments on TB-Dense and MATRES datasets demonstrate that our approach outperforms existing methods.