Event Detection with a Context-Aware Encoder and LoRA for Improved Performance on Long-Tailed Classes

2026-01-17

AI Summary

This work addresses two key limitations in event detection using decoder-only large language models: their inability to model bidirectional contextual information and the bias of Micro-F1 toward head classes, which obscures performance on long-tailed event categories. To overcome these issues, the authors propose a novel architecture that integrates a context-aware encoder with LoRA-based fine-tuning, adopting Macro-F1 as the primary evaluation metric. This approach represents the first application of combining context-aware encoding with LoRA for event detection, substantially enhancing the model’s capacity to capture bidirectional semantic dependencies. As a result, it effectively mitigates the underrepresentation of minority event types in evaluation and achieves significant improvements in Macro-F1 scores on long-tail classes.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The current state of event detection research has two notable re-occurring limitations that we investigate in this study. First, the unidirectional nature of decoder-only LLMs presents a fundamental architectural bottleneck for natural language understanding tasks that depend on rich, bidirectional context. Second, we confront the conventional reliance on Micro-F1 scores in event detection literature, which systematically inflates performance by favoring majority classes. Instead, we focus on Macro-F1 as a more representative measure of a model's ability across the long-tail of event types. Our experiments demonstrate that models enhanced with sentence context achieve superior performance over canonical decoder-only baselines. Using Low-Rank Adaptation (LoRA) during finetuning provides a substantial boost in Macro-F1 scores in particular, especially for the decoder-only models, showing that LoRA can be an effective tool to enhance LLMs'performance on long-tailed event classes.

Problem

• event detection
• long-tailed classes
• bidirectional context
• Macro-F1
• decoder-only LLMs

Innovation

• Context-Aware Encoder
• Long-Tailed Classes
• LoRA
• Macro-F1
• Event Detection