Papers with LLM-based augmentation
CEAN: Contrastive Event Aggregation Network with LLM-based Augmentation for Event Extraction (2024.eacl-long)
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| Challenge: | Event Extraction is a crucial yet arduous task in natural language processing (NLP), as its performance is hindered by laborious data annotation. |
| Approach: | They propose a Contrastive Event Aggregation Network with LLM-based Augmentation to promote low-resource learning and reduce data noise for event extraction. |
| Outcome: | The proposed approach achieves new state-of-the-art results on the ACE2005 and ERE-EN datasets. |
Beyond Guilt: Legal Judgment Prediction with Trichotomous Reasoning (2025.findings-emnlp)
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| Challenge: | Current legal large language models lack trichotomous reasoning capabilities due to the absence of an appropriate benchmark dataset. |
| Approach: | They propose a benchmark dataset for Legal Judgment Prediction with Innocent Verdicts that incorporates trichotomous dogmatics into zero-shot prompting and fine-tuning. |
| Outcome: | The proposed dataset extends three widely-used legal datasets through LLM-based augmentation and manual verification. |
Into the Unknown: Generating Geospatial Descriptions for New Environments (2024.findings-acl)
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| Challenge: | Similar to vision-and-language navigation tasks, the Rendezvous (RVS) task requires reasoning over allocentric spatial relationships using non-sequential navigation instructions and maps. |
| Approach: | They propose a large-scale augmentation method for generating high-quality synthetic data for new environments using readily available geospatial data. |
| Outcome: | The proposed method improves accuracy on unseen and seen environments by 45.83% on the Rendezvous (RVS) task. |
LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs? (2025.naacl-long)
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| Challenge: | Recent studies have compared LLM-based augmentations with established methods, but the results are contradictory. |
| Approach: | They compare the performance of LLM-based augmentation methods with established ones . they found that LLMs are worthy of deployment only when very small number of seeds is used . |
| Outcome: | The proposed methods are worthy of deployment only when very small number of seeds is used. |