Event Detection with a Context-Aware Encoder and LoRA for Improved Performance on Long-Tailed Classes
Year: 2026
Type: conference-paper
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 decoderonly 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.
Institution: University of South Florida
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140113711