Benchmarking Large Language Models for Chinese and Japanese IMEs: Phonetic-to-Character Generation and Textual Error Correction

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.

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