Papers by Oliver Boyd
GLiNER2: Schema-Driven Multi-Task Learning for Structured Information Extraction (2025.emnlp-demos)
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| Challenge: | Existing solutions for information extraction (IE) require specialized models for different tasks or require expensive large language models. |
| Approach: | They propose a framework that enhances the original GLiNER architecture to support named entity recognition, text classification, and hierarchical structured data extraction within a single efficient model. |
| Outcome: | The proposed framework improves performance across diverse IE tasks and accessibility compared to LLM-based alternatives. |