LAMPEAE: LLM-Augmented Manifold Probing for Adaptive Event Argument Extraction

ACL ARR 2026 January Submission6584 Authors

Keywords: Event Argument Extraction, Large Language Models, Manifold Learning, Low-Rank Adaptation

Abstract: Event Argument Extraction (EAE) aims to extract arguments for specified events from a text. Previous prompt-based research has mainly focused on designing static role anchors, effectively modeling general semantics but overlooking the geometric rigidity of these representations: (i) they failed to capture the diverse manifold structure of real-world semantic distributions and (ii) they lacked the flexibility to adapt to instance-specific reasoning contexts. To bridge the gap between static parameters and dynamic contexts, we introduce a new framework named LAMPEAE, which reformulates EAE as a dynamic manifold matching problem via adaptive parameter instantiation. Specifically, we employ a neuro-symbolic approach that utilizes a frozen LLM to extract high-order reasoning priors. A lightweight hypernetwork then maps this meta-knowledge into low-rank geometric transformation matrices, which dynamically project original static prompts into instance-specific probes. This mechanism ensures precise semantic alignment within the continuous parameter space. Experimental results on the RAMS, ACE05, and WikiEvent benchmarks show that LAMPEAE establishes new state-of-the-art performance, respectively, effectively validating its superiority in handling heterogeneous semantic transfer.

Paper Type: Long

Research Area: Information Extraction and Retrieval

Research Area Keywords: event extraction

Contribution Types: Model analysis & interpretability, Approaches to low-resource settings, Data analysis, Theory

Languages Studied: English

Submission Number: 6584