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.
@article{arxiv.2601.11932,
title = {Event Detection with a Context-Aware Encoder and LoRA for Improved Performance on Long-Tailed Classes},
author = {Abdullah Al Monsur and Nitesh Vamshi Bommisetty and Gene Louis Kim},
journal= {arXiv preprint arXiv:2601.11932},
year = {2026}
}