Large Language Models for Data Annotation and Synthesis: A Survey
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917853967417344 |
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| author | Tan, Zhen Li, Dawei Wang, Song Beigi, Alimohammad Jiang, Bohan Bhattacharjee, Amrita Karami, Mansooreh Li, Jundong Cheng, Lu Liu, Huan |
| author_facet | Tan, Zhen Li, Dawei Wang, Song Beigi, Alimohammad Jiang, Bohan Bhattacharjee, Amrita Karami, Mansooreh Li, Jundong Cheng, Lu Liu, Huan |
| contents | Data annotation and synthesis generally refers to the labeling or generating of raw data with relevant information, which could be used for improving the efficacy of machine learning models. The process, however, is labor-intensive and costly. The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4, presents an unprecedented opportunity to automate the complicated process of data annotation and synthesis. While existing surveys have extensively covered LLM architecture, training, and general applications, we uniquely focus on their specific utility for data annotation. This survey contributes to three core aspects: LLM-Based Annotation Generation, LLM-Generated Annotations Assessment, and LLM-Generated Annotations Utilization. Furthermore, this survey includes an in-depth taxonomy of data types that LLMs can annotate, a comprehensive review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation and synthesis. Serving as a key guide, this survey aims to assist researchers and practitioners in exploring the potential of the latest LLMs for data annotation, thereby fostering future advancements in this critical field. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_13446 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models for Data Annotation and Synthesis: A Survey Tan, Zhen Li, Dawei Wang, Song Beigi, Alimohammad Jiang, Bohan Bhattacharjee, Amrita Karami, Mansooreh Li, Jundong Cheng, Lu Liu, Huan Computation and Language Data annotation and synthesis generally refers to the labeling or generating of raw data with relevant information, which could be used for improving the efficacy of machine learning models. The process, however, is labor-intensive and costly. The emergence of advanced Large Language Models (LLMs), exemplified by GPT-4, presents an unprecedented opportunity to automate the complicated process of data annotation and synthesis. While existing surveys have extensively covered LLM architecture, training, and general applications, we uniquely focus on their specific utility for data annotation. This survey contributes to three core aspects: LLM-Based Annotation Generation, LLM-Generated Annotations Assessment, and LLM-Generated Annotations Utilization. Furthermore, this survey includes an in-depth taxonomy of data types that LLMs can annotate, a comprehensive review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation and synthesis. Serving as a key guide, this survey aims to assist researchers and practitioners in exploring the potential of the latest LLMs for data annotation, thereby fostering future advancements in this critical field. |
| title | Large Language Models for Data Annotation and Synthesis: A Survey |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.13446 |