In this study, we investigate in-context learning (ICL) in document-level
event argument extraction (EAE). The paper identifies key challenges in this
problem, including example selection, context length limitation, abundance of
event types, and the limitation of Chain-of-Thought (CoT) prompting in
non-reasoning tasks. To address these challenges, we introduce the
Heuristic-Driven Link-of-Analogy (HD-LoA) prompting method. Specifically, we
hypothesize and validate that LLMs learn task-specific heuristics from
demonstrations via ICL. Building upon this hypothesis, we introduce an explicit
heuristic-driven demonstration construction approach, which transforms the
haphazard example selection process into a methodical method that emphasizes
task heuristics. Additionally, inspired by the analogical reasoning of human,
we propose the link-of-analogy prompting, which enables LLMs to process new
situations by drawing analogies to known situations, enhancing their
adaptability. Extensive experiments show that our method outperforms the
existing prompting methods and few-shot supervised learning methods, exhibiting
F1 score improvements of 4.53% and 9.38% on the document-level EAE dataset.
Furthermore, when applied to sentiment analysis and natural language inference
tasks, the HD-LoA prompting achieves accuracy gains of 2.87% and 2.63%,
indicating its effectiveness across different tasks