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arXiv · 2306.14377

Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction

Chanjun Park·Seonmin Koo·Seolhwa Lee·Jaehyung Seo·Sugyeong Eo·Hyeonseok Moon·Heuiseok Lim
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Abstract

Data-centric AI approach aims to enhance the model performance without modifying the model and has been shown to impact model performance positively. While recent attention has been given to data-centric AI based on synthetic data, due to its potential for performance improvement, data-centric AI has long been exclusively validated using real-world data and publicly available benchmark datasets. In respect of this, data-centric AI still highly depends on real-world data, and the verification of models using synthetic data has not yet been thoroughly carried out. Given the challenges above, we ask the question: Does data quality control (noise injection and balanced data), a data-centric AI methodology acclaimed to have a positive impact, exhibit the same positive impact in models trained solely with synthetic data? To address this question, we conducted comparative analyses between models trained on synthetic and real-world data based on grammatical error correction (GEC) task. Our experimental results reveal that the data quality control method has a positive impact on models trained with real-world data, as previously reported in existing studies, while a negative impact is observed in models trained solely on synthetic data.

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Chanjun Park, Seonmin Koo, Seolhwa Lee, Jaehyung Seo, Sugyeong Eo, Hyeonseok Moon, Heuiseok Lim. 2023-06-26. Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction. https://arxiv.org/abs/2306.14377

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