@inproceedings{long-etal-2024-llms,
title = "On {LLM}s-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey",
author = "Long, Lin and
Wang, Rui and
Xiao, Ruixuan and
Zhao, Junbo and
Ding, Xiao and
Chen, Gang and
Wang, Haobo",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.658",
pages = "11065--11082",
abstract = "Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models (LLMs) offers a data-centric solution to alleviate the limitations of real-world data with synthetic data generation. However, current investigations into this field lack a unified framework and mostly stay on the surface. Therefore, this paper provides an organization of relevant studies based on a generic workflow of synthetic data generation. By doing so, we highlight the gaps within existing research and outline prospective avenues for future study. This work aims to shepherd the academic and industrial communities towards deeper, more methodical inquiries into the capabilities and applications of LLMs-driven synthetic data generation.",
}
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<abstract>Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models (LLMs) offers a data-centric solution to alleviate the limitations of real-world data with synthetic data generation. However, current investigations into this field lack a unified framework and mostly stay on the surface. Therefore, this paper provides an organization of relevant studies based on a generic workflow of synthetic data generation. By doing so, we highlight the gaps within existing research and outline prospective avenues for future study. This work aims to shepherd the academic and industrial communities towards deeper, more methodical inquiries into the capabilities and applications of LLMs-driven synthetic data generation.</abstract>
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%0 Conference Proceedings
%T On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey
%A Long, Lin
%A Wang, Rui
%A Xiao, Ruixuan
%A Zhao, Junbo
%A Ding, Xiao
%A Chen, Gang
%A Wang, Haobo
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F long-etal-2024-llms
%X Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models (LLMs) offers a data-centric solution to alleviate the limitations of real-world data with synthetic data generation. However, current investigations into this field lack a unified framework and mostly stay on the surface. Therefore, this paper provides an organization of relevant studies based on a generic workflow of synthetic data generation. By doing so, we highlight the gaps within existing research and outline prospective avenues for future study. This work aims to shepherd the academic and industrial communities towards deeper, more methodical inquiries into the capabilities and applications of LLMs-driven synthetic data generation.
%U https://aclanthology.org/2024.findings-acl.658
%P 11065-11082
Markdown (Informal)
[On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://aclanthology.org/2024.findings-acl.658) (Long et al., Findings 2024)
ACL
- Lin Long, Rui Wang, Ruixuan Xiao, Junbo Zhao, Xiao Ding, Gang Chen, and Haobo Wang. 2024. On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey. In Findings of the Association for Computational Linguistics ACL 2024, pages 11065–11082, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.