Instruction-Tuning LLMs for Event Extraction with Annotation Guidelines
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916765258219520 |
|---|---|
| author | Srivastava, Saurabh Pati, Sweta Yao, Ziyu |
| author_facet | Srivastava, Saurabh Pati, Sweta Yao, Ziyu |
| contents | In this work, we study the effect of annotation guidelines -- textual descriptions of event types and arguments, when instruction-tuning large language models for event extraction. We conducted a series of experiments with both human-provided and machine-generated guidelines in both full- and low-data settings. Our results demonstrate the promise of annotation guidelines when there is a decent amount of training data and highlight its effectiveness in improving cross-schema generalization and low-frequency event-type performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16377 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Instruction-Tuning LLMs for Event Extraction with Annotation Guidelines Srivastava, Saurabh Pati, Sweta Yao, Ziyu Computation and Language In this work, we study the effect of annotation guidelines -- textual descriptions of event types and arguments, when instruction-tuning large language models for event extraction. We conducted a series of experiments with both human-provided and machine-generated guidelines in both full- and low-data settings. Our results demonstrate the promise of annotation guidelines when there is a decent amount of training data and highlight its effectiveness in improving cross-schema generalization and low-frequency event-type performance. |
| title | Instruction-Tuning LLMs for Event Extraction with Annotation Guidelines |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.16377 |