Papers with BioRED
Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction (2025.naacl-long)
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| Challenge: | Recent research has explored the potential of leveraging natural language inference (NLI) techniques to enhance relation extraction (RE). |
| Approach: | They propose a method that verbalizes relation classes into class-indicative hypotheses to align a traditionally multi-class classification task to one of textual entailment. |
| Outcome: | The proposed method improves relation extraction performance on BioRED and ReTACRED. |
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction (2023.acl-long)
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| Challenge: | Relation extraction (RE) has been challenging in low-resource domains and with limited resources. |
| Approach: | They propose to pretrain and finetune the RE model using consistent objectives of contrastive learning. |
| Outcome: | The proposed method outperforms PLM-based RE classifier on two document-level RE datasets. |
Enhanced Reasoning for Biomedical Document-Level Relation Extraction via a Novel Cascade Language Model Framework (2026.acl-long)
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| Challenge: | Pre-trained language models (PLMs) are the leading paradigm in document-level relation extraction. |
| Approach: | They propose a cascade framework that leverages the complementary strengths of PLMs and LLMs through a detect-then-rethink paradigm. |
| Outcome: | The proposed framework improves on BioRED and CDR datasets and improves existing models. |
Refining and Reusing Annotation Guidelines for LLM Annotation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) demonstrates remarkable zero-shot annotation tasks . but, they struggle with the specialized conventions of gold-standard benchmarks . |
| Approach: | They propose to reuse and refine annotation guidelines as an alignment mechanism . they propose to use iterative moderation framework to simulate early phases of annotation projects . |
| Outcome: | The proposed framework shows a good potential in effectively refining guidelines, but there is room for improvement. |