@inproceedings{zou-etal-2026-benchmarking,
    title = "Benchmarking Large Language Models for {C}hinese and {J}apanese {IME}s: Phonetic-to-Character Generation and Textual Error Correction",
    author = "Zou, Yuchun  and
      Lee, Tedd  and
      Fan, Xiaodi  and
      Li, Jun",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.lrec-1.337/",
    doi = "10.63317/42jiimjriyga",
    pages = "4290--4311",
    abstract = "Efficient text entry for complex writing systems like Chinese and Japanese necessitates the use of Input Method Editors (IMEs). While Large Language Models (LLMs) are emerging as powerful, context-aware language resources for this task, we present a comprehensive benchmark and evaluation methodology to assess the viability of LLMs for next-generation IMEs. We conduct a comparative analysis of a diverse set of LLMs against established baseline methods on two core tasks: phonetic-to-character generation (using Pinyin and Romaji) and textual error correction. Our experiments demonstrate that top-tier LLMs achieve superior accuracy by leveraging deep contextual understanding, significantly outperforming traditional systems in ambiguity resolution and the correction of complex errors. However, our analysis also reveals a crucial trade-off between accuracy and computational efficiency across different models. The datasets, evaluation scripts, and results from this study serve as a vital public resource for future research, providing a robust baseline for developing and selecting models that balance performance with the low-latency demands of real-world text input."
}