ULTRA: Unleash LLMs' Potential for Event Argument Extraction through
  Hierarchical Modeling and Pair-wise Refinement
Xinliang Frederick Zhang Carter Blum Temma Choji Shalin Shah Alakananda Vempala
Structural extraction of events within discourse is critical since it avails
a deeper understanding of communication patterns and behavior trends. Event
argument extraction (EAE), at the core of event-centric understanding, is the
task of identifying role-specific text spans (i.e., arguments) for a given
event. Document-level EAE (DocEAE) focuses on arguments that are scattered
across an entire document. In this work, we explore the capabilities of open
source Large Language Models (LLMs), i.e., Flan-UL2, for the DocEAE task. To
this end, we propose ULTRA, a hierarchical framework that extracts event
arguments more cost-effectively -- the method needs as few as 50 annotations
and doesn't require hitting costly API endpoints. Further, it alleviates the
positional bias issue intrinsic to LLMs. ULTRA first sequentially reads text
chunks of a document to generate a candidate argument set, upon which ULTRA
learns to drop non-pertinent candidates through self-refinement. We further
introduce LEAFER to address the challenge LLMs face in locating the exact
boundary of an argument span. ULTRA outperforms strong baselines, which include
strong supervised models and ChatGPT, by 9.8% when evaluated by the exact match
(EM) metric.