Revisiting Acceptability Judgements

Hai Hu; Ziyin Zhang; Weifang Huang; Jackie Yan-Ki Lai; Aini Li; Yina Patterson; Jiahui Huang; Peng Zhang; Chien-Jer Charles Lin; Rui Wang

arXiv · 2305.14091

Revisiting Acceptability Judgements

Hai Hu·Ziyin Zhang·Weifang Huang·Jackie Yan-Ki Lai·Aini Li·Yina Patterson·Jiahui Huang·Peng Zhang·Chien-Jer Charles Lin·Rui Wang

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Abstract

In this work, we revisit linguistic acceptability in the context of large language models. We introduce CoLAC - Corpus of Linguistic Acceptability in Chinese, the first large-scale acceptability dataset for a non-Indo-European language. It is verified by native speakers and is the first acceptability dataset that comes with two sets of labels: a linguist label and a crowd label. Our experiments show that even the largest InstructGPT model performs only at chance level on CoLAC, while ChatGPT's performance (48.30 MCC) is also much below supervised models (59.03 MCC) and human (65.11 MCC). Through cross-lingual transfer experiments and fine-grained linguistic analysis, we provide detailed analysis of the model predictions and demonstrate for the first time that knowledge of linguistic acceptability can be transferred across typologically distinct languages, as well as be traced back to pre-training. Our dataset is publicly available at \url{https://github.com/huhailinguist/CoLAC}.

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Hai Hu, Ziyin Zhang, Weifang Huang, Jackie Yan-Ki Lai, Aini Li, Yina Patterson, Jiahui Huang, Peng Zhang, Chien-Jer Charles Lin, Rui Wang. 2023-05-23. Revisiting Acceptability Judgements. https://arxiv.org/abs/2305.14091

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