# GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction

arXiv: https://arxiv.org/abs/2508.20828 · HTML: https://mlsanity.com/paper/2508.20828/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 21 of 21)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [CF-VLM:CounterFactual Vision-Language Fine-tuning](https://mlsanity.com/paper/2506.17267/) (2025)
- [GAM-Agent: Game-Theoretic and Uncertainty-Aware Collaboration for Complex Visual Reasoning](https://mlsanity.com/paper/2505.23399/) (2025)
- [Joint Constrained Learning for Event-Event Relation Extraction](https://mlsanity.com/paper/2010.06727/) (2020)
- [Zero-shot Temporal Relation Extraction with ChatGPT](https://mlsanity.com/paper/2304.05454/) (2023)
- [PersonaX: A Recommendation Agent Oriented User Modeling Framework for Long Behavior Sequence](https://mlsanity.com/paper/2503.02398/) (2025)
- [An Improved Neural Baseline for Temporal Relation Extraction](https://mlsanity.com/paper/1909.00429/) (2019)
- [Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding](https://mlsanity.com/paper/2502.06020/) (2025)
- [Deep Structured Neural Network for Event Temporal Relation Extraction](https://mlsanity.com/paper/1909.10094/) (2019)
- [Extracting Temporal Event Relation with Syntax-guided Graph Transformer](https://mlsanity.com/paper/2104.09570/) (2021)
- [Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction](https://mlsanity.com/paper/2009.07373/) (2020)
- [More than Classification: A Unified Framework for Event Temporal Relation Extraction](https://mlsanity.com/paper/2305.17607/) (2023)
- [Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems](https://mlsanity.com/paper/2407.10670/) (2024)
- [Extracting Event Temporal Relations via Hyperbolic Geometry](https://mlsanity.com/paper/2109.05527/) (2021)
- [Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events](https://mlsanity.com/paper/2004.04295/) (2020)
- [Event Temporal Relation Extraction with Bayesian Translational Model](https://mlsanity.com/paper/2302.04985/) (2023)
- [Position Bias Mitigates Position Bias:Mitigate Position Bias Through Inter-Position Knowledge Distillation](https://mlsanity.com/paper/2508.15709/) (2025)
- [Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction](https://mlsanity.com/paper/2209.00568/) (2022)
- [Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning](https://mlsanity.com/paper/2310.20236/) (2023)
- [Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction](https://mlsanity.com/paper/2004.06216/) (2020)

## Built on by (most cited first, 2 of 2)

- [Graph is a Substrate Across Data Modalities](https://mlsanity.com/paper/2601.22384/) (2026)
- [Event Interaction in Low-Rank Bottlenecks for Temporal Relation Extraction](https://mlsanity.com/paper/2609.06731/) (2026)

## Abstract (arXiv)

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.