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            <namePart type="given">Alexis</namePart>
            <namePart type="family">Palmer</namePart>
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    <abstract>Using Japanese honorifics is challenging because it requires not only knowledge of the grammatical rules but also contextual information, such as social relationships. It remains unclear whether pre-trained large language models (LLMs) can flexibly handle Japanese honorifics like humans. To analyze this, we introduce an honorific conversion task that considers social relationships among people mentioned in a conversation. We construct a Japanese honorifics dataset from problem templates of various sentence structures to investigate the syntactic generalization capacity of GPT-3, one of the leading LLMs, on this task under two settings: fine-tuning and prompt learning. Our results showed that the fine-tuned GPT-3 performed better in a context-aware honorific conversion task than the prompt-based one. The fine-tuned model demonstrated overall syntactic generalizability towards compound honorific sentences, except when tested with the data involving direct speech.</abstract>
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    <identifier type="doi">10.18653/v1/2023.starsem-1.5</identifier>
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%0 Conference Proceedings
%T Analyzing Syntactic Generalization Capacity of Pre-trained Language Models on Japanese Honorific Conversion
%A Sekizawa, Ryo
%A Yanaka, Hitomi
%Y Palmer, Alexis
%Y Camacho-collados, Jose
%S Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sekizawa-yanaka-2023-analyzing
%X Using Japanese honorifics is challenging because it requires not only knowledge of the grammatical rules but also contextual information, such as social relationships. It remains unclear whether pre-trained large language models (LLMs) can flexibly handle Japanese honorifics like humans. To analyze this, we introduce an honorific conversion task that considers social relationships among people mentioned in a conversation. We construct a Japanese honorifics dataset from problem templates of various sentence structures to investigate the syntactic generalization capacity of GPT-3, one of the leading LLMs, on this task under two settings: fine-tuning and prompt learning. Our results showed that the fine-tuned GPT-3 performed better in a context-aware honorific conversion task than the prompt-based one. The fine-tuned model demonstrated overall syntactic generalizability towards compound honorific sentences, except when tested with the data involving direct speech.
%R 10.18653/v1/2023.starsem-1.5
%U https://aclanthology.org/2023.starsem-1.5
%U https://doi.org/10.18653/v1/2023.starsem-1.5
%P 40-47

Markdown (Informal)

[Analyzing Syntactic Generalization Capacity of Pre-trained Language Models on Japanese Honorific Conversion](https://aclanthology.org/2023.starsem-1.5) (Sekizawa & Yanaka, *SEM 2023)

• Analyzing Syntactic Generalization Capacity of Pre-trained Language Models on Japanese Honorific Conversion (Sekizawa & Yanaka, *SEM 2023)

ACL

• Ryo Sekizawa and Hitomi Yanaka. 2023. Analyzing Syntactic Generalization Capacity of Pre-trained Language Models on Japanese Honorific Conversion. In Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023), pages 40–47, Toronto, Canada. Association for Computational Linguistics.