Papers with ZSRE
Towards Scalable Lifelong Knowledge Editing with Selective Knowledge Suppression (2026.acl-long)
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| Challenge: | Existing methods to modify knowledge are limited due to high training costs and lack stability during sequential edits due to catastrophic forgetting. |
| Approach: | They propose a framework to modify specific knowledge of large language models without retraining the entire model. |
| Outcome: | Extensive experiments on ZSRE, Counterfact, and RIPE show that LightEdit outperforms existing lifelong knowledge editing methods. |
AlignRE: An Encoding and Semantic Alignment Approach for Zero-Shot Relation Extraction (2024.findings-acl)
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| Challenge: | Existing prototype-based methods for ZSRE ignore abundant side information and suffer from a significant encoding gap between prototypes and sentences. |
| Approach: | They propose a framework to encode schema alignment to enhance prototype-based ZSRE methods. |
| Outcome: | The proposed method outperforms existing methods on FewRel and Wiki-ZSL datasets and exhibits substantially faster performance and reduces the need for extensive manual labor in prototype construction. |
Document-Level Zero-Shot Relation Extraction with Entity Side Information (2026.eacl-long)
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| Challenge: | Existing approaches rely on Large Language Models (LLMs) to generate synthetic data for unseen labels. |
| Approach: | They propose a document-level zero-shot relation extraction framework with Entity Side Information to solve existing problems. |
| Outcome: | The proposed approach achieves an average improvement of 11.6% in the macro F1-Score compared to baseline models and existing benchmarks. |
CE-DA: Custom Embedding and Dynamic Aggregation for Zero-Shot Relation Extraction (2025.coling-main)
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| Challenge: | Existing methods to predict relationships with given entity pairs are lacking in supervised methods. |
| Approach: | They propose a framework for zero-shot Relation Extraction that includes two modules: Custom Embedding and Dynamic Aggregation. |
| Outcome: | The proposed framework shows competitive performance on two ZSRE datasets. |
Generation-Augmented Retrieval: Rethinking the Role of Large Language Models in Zero-Shot Relation Extraction (2025.findings-emnlp)
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| Challenge: | Recent advances in Relation Extraction (RE) emphasize Zero-Shot methodologies, aiming to recognize unseen relations between entities with no annotated data. |
| Approach: | They propose a plug-in retrieval adjuster that allows rapid fine-tuning without accessing LLMs’ parameters. |
| Outcome: | The proposed model demonstrates comparable performance on multiple benchmarks. |
From Local Perspective to Global Reasoning: A Neuro-Symbolic Framework for Zero-Shot Relation Extraction (2026.findings-acl)
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| Challenge: | Existing methods for zero-shot relationship extraction do not distinguish between unseen, semantically similar relations. |
| Approach: | They propose a framework to enable global reasoning across a set of predictions. |
| Outcome: | The proposed framework outperforms existing methods and establishes new state-of-the-art results on widely used datasets. |