
This research paper introduces GPAM, a novel framework for few-shot relation extraction with NOTA (none-of-the-above), addressing the challenges of limited data and unknown relation classification by employing Gaussian prototype and adaptive margin techniques.


coremsg

few-shot-open-relation-extraction-with-gaussian-prototype-and-adaptive-margin-a-novel-approach-to-identifying-known-and-unknown-relations-in-low-resource-settings


Few-Shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin: A Novel Approach to Identifying Known and Unknown Relations in Low-Resource Settings


title_rewrite


This work introduces a meta dataset for few-shot relation extraction that captures realistic real-world scenarios, and conducts a comprehensive evaluation of six recent few-shot relation extraction methods, revealing the need for substantial future research in this domain.


towards-realistic-few-shot-relation-extraction-a-new-meta-dataset-and-comprehensive-evaluation


Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Comprehensive Evaluation



Large language models can achieve competitive performance in few-shot relation extraction tasks using the CoT-ER approach, which incorporates explicit evidence reasoning.


explicit-evidence-reasoning-for-few-shot-relation-extraction-with-chain-of-thought-approach


Explicit Evidence Reasoning for Few-shot Relation Extraction with Chain of Thought Approach
