Reasoning-Oriented and Analogy-Based Methods for Locating and Editing in Zero-Shot Event-Relational Reasoning

2025-01-01

AI Summary

Existing event relation reasoning methods suffer from high computational overhead, poor interpretability, and limited cross-task knowledge transfer—especially under zero-shot settings. To address these challenges, we propose a training-free, module-level localization and editing framework comprising two novel components: Reasoning-Oriented Localization (ROLE) and Analogy-Based Editing (ABLE). ROLE identifies critical model modules via attention patterns and gradient sensitivity, drastically reducing computation while enhancing reasoning transparency. ABLE enables efficient zero-shot knowledge transfer by establishing event-relation analogies and aligning inference paths across tasks. Experimental results demonstrate that ABLE achieves state-of-the-art performance on zero-shot event relation reasoning. Overall, our framework jointly advances efficiency, interpretability, and generalization—overcoming key limitations of prior approaches.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Zero-shot event-relational reasoning is an important task in natural language processing, and existing methods jointly learn a variety of event-relational prefixes and inference-form prefixes to achieve such tasks. However, training prefixes consumes large computational resources and lacks interpretability. Additionally, learning various relational and inferential knowledge inefficiently exploits the connections between tasks. Therefore, we first propose a method for Reasoning-Oriented Locating and Editing (ROLE), which locates and edits the key modules of the language model for reasoning about event relations, enhancing interpretability and also resource-efficiently optimizing the reasoning ability. Subsequently, we propose a method for Analogy-Based Locating and Editing (ABLE), which efficiently exploits the similarities and differences between tasks to optimize the zero-shot reasoning capability. Experimental results show that ROLE improves interpretability and reasoning performance with reduced computational cost. ABLE achieves SOTA results in zero-shot reasoning.

Problem

• Event Relation Judgment
• Computational Resource Efficiency
• Cross-Task Knowledge Utilization

Innovation

• ROLE
• ABLE
• Cross-task Learning