Since the release of ChatGPT, generative models have achieved tremendous success and become the de facto approach for various NLP tasks. However, its application in the field of input methods remains under-explored. Many neural network approaches have been applied to the construction of Chinese input method engines(IMEs).Previous research often assumed that the input pinyin was correct and focused on Pinyin-to-character(P2C) task, which significantly falls short of meeting users' demands. Moreover, previous research could not leverage user feedback to optimize the model and provide personalized results. In this study, we propose a novel Generative Input paradigm named GeneInput. It uses prompts to handle all input scenarios and other intelligent auxiliary input functions, optimizing the model with user feedback to deliver personalized results. The results demonstrate that we have achieved state-of-the-art performance for the first time in the Full-mode Key-sequence to Characters(FK2C) task. We propose a novel reward model training method that eliminates the need for additional manual annotations and the performance surpasses GPT-4 in tasks involving intelligent association and conversational assistance. Compared to traditional paradigms, GeneInput not only demonstrates superior performance but also exhibits enhanced robustness, scalability, and online learning capabilities.

本研究提出了一种名为GeneInput的新型生成输入范式，通过使用提示来处理所有输入场景和其他智能辅助输入功能，并通过用户反馈优化模型以提供个性化结果。结果表明，在全模式键序列到字符（FK2C）任务中，我们首次实现了最先进的性能。我们提出了一种新的奖励模型训练方法，消除了额外的手动注释需求，并在涉及智能关联和对话辅助的任务中超越了GPT-4性能。与传统范式相比，GeneInput不仅展示了卓越的性能，而且具有增强的鲁棒性、可扩展性和在线学习能力。

生成输入：走向下一代的输入方法范式