@inproceedings{kim-etal-2025-evaluating,
title = "Evaluating Language Models as Synthetic Data Generators",
author = "Kim, Seungone and
Suk, Juyoung and
Yue, Xiang and
Viswanathan, Vijay and
Lee, Seongyun and
Wang, Yizhong and
Gashteovski, Kiril and
Lawrence, Carolin and
Welleck, Sean and
Neubig, Graham",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.320/",
doi = "10.18653/v1/2025.acl-long.320",
pages = "6385--6403",
ISBN = "979-8-89176-251-0",
abstract = "Given the increasing use of synthetic data in language model (LM) post-training, an LM{'}s ability to generate high-quality data has become nearly as crucial as its ability to solve problems directly. While prior works have focused on developing effective data generation methods, they lack systematic comparison of different LMs as data generators in a unified setting. To address this gap, we propose AgoraBench, a benchmark that provides standardized settings and metrics to evaluate LMs' data generation abilities. Through synthesizing 1.26 million training instances using 6 LMs and training 99 student models, we uncover key insights about LMs' data generation capabilities. First, we observe that LMs exhibit distinct strengths. For instance, GPT-4o excels at generating new problems, while Claude-3.5-Sonnet performs better at enhancing existing ones. Furthermore, our analysis reveals that an LM{'}s data generation ability doesn{'}t necessarily correlate with its problem-solving ability. Instead, multiple intrinsic features of data quality{---}including response quality, perplexity, and instruction difficulty{---}collectively serve as better indicators. Finally, we demonstrate that strategic choices in output format and cost-conscious model selection significantly impact data generation effectiveness. Our code, checkpoints, and data are all publicly available at https://github.com/neulab/data-agora."
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%0 Conference Proceedings
%T Evaluating Language Models as Synthetic Data Generators
%A Kim, Seungone
%A Suk, Juyoung
%A Yue, Xiang
%A Viswanathan, Vijay
%A Lee, Seongyun
%A Wang, Yizhong
%A Gashteovski, Kiril
%A Lawrence, Carolin
%A Welleck, Sean
%A Neubig, Graham
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F kim-etal-2025-evaluating
%X Given the increasing use of synthetic data in language model (LM) post-training, an LM’s ability to generate high-quality data has become nearly as crucial as its ability to solve problems directly. While prior works have focused on developing effective data generation methods, they lack systematic comparison of different LMs as data generators in a unified setting. To address this gap, we propose AgoraBench, a benchmark that provides standardized settings and metrics to evaluate LMs’ data generation abilities. Through synthesizing 1.26 million training instances using 6 LMs and training 99 student models, we uncover key insights about LMs’ data generation capabilities. First, we observe that LMs exhibit distinct strengths. For instance, GPT-4o excels at generating new problems, while Claude-3.5-Sonnet performs better at enhancing existing ones. Furthermore, our analysis reveals that an LM’s data generation ability doesn’t necessarily correlate with its problem-solving ability. Instead, multiple intrinsic features of data quality—including response quality, perplexity, and instruction difficulty—collectively serve as better indicators. Finally, we demonstrate that strategic choices in output format and cost-conscious model selection significantly impact data generation effectiveness. Our code, checkpoints, and data are all publicly available at https://github.com/neulab/data-agora.
%R 10.18653/v1/2025.acl-long.320
%U https://aclanthology.org/2025.acl-long.320/
%U https://doi.org/10.18653/v1/2025.acl-long.320
%P 6385-6403
Markdown (Informal)
[Evaluating Language Models as Synthetic Data Generators](https://aclanthology.org/2025.acl-long.320/) (Kim et al., ACL 2025)
ACL
- Seungone Kim, Juyoung Suk, Xiang Yue, Vijay Viswanathan, Seongyun Lee, Yizhong Wang, Kiril Gashteovski, Carolin Lawrence, Sean Welleck, and Graham Neubig. 2025. Evaluating Language Models as Synthetic Data Generators. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6385–6403, Vienna, Austria. Association for Computational Linguistics.