Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913500388917248 |
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| author | Wu, Jianfei Wang, Xubin Jia, Weijia |
| author_facet | Wu, Jianfei Wang, Xubin Jia, Weijia |
| contents | The traditional data annotation process is often labor-intensive, time-consuming, and susceptible to human bias, which complicates the management of increasingly complex datasets. This study explores the potential of large language models (LLMs) as automated data annotators to improve efficiency and consistency in annotation tasks. By employing rationale-driven collaborative few-shot prompting techniques, we aim to improve the performance of LLMs in text annotation. We conduct a rigorous evaluation of six LLMs across four benchmark datasets, comparing seven distinct methodologies. Our results demonstrate that collaborative methods consistently outperform traditional few-shot techniques and other baseline approaches, particularly in complex annotation tasks. Our work provides valuable insights and a robust framework for leveraging collaborative learning methods to tackle challenging text annotation tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09615 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting Wu, Jianfei Wang, Xubin Jia, Weijia Computation and Language Artificial Intelligence The traditional data annotation process is often labor-intensive, time-consuming, and susceptible to human bias, which complicates the management of increasingly complex datasets. This study explores the potential of large language models (LLMs) as automated data annotators to improve efficiency and consistency in annotation tasks. By employing rationale-driven collaborative few-shot prompting techniques, we aim to improve the performance of LLMs in text annotation. We conduct a rigorous evaluation of six LLMs across four benchmark datasets, comparing seven distinct methodologies. Our results demonstrate that collaborative methods consistently outperform traditional few-shot techniques and other baseline approaches, particularly in complex annotation tasks. Our work provides valuable insights and a robust framework for leveraging collaborative learning methods to tackle challenging text annotation tasks. |
| title | Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2409.09615 |