# Generative Input: Towards Next-Generation Input Methods Paradigm

arXiv: https://arxiv.org/abs/2311.01166 · HTML: https://mlsanity.com/paper/2311.01166/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019)
- [Exploring and Adapting Chinese GPT to Pinyin Input Method](https://mlsanity.com/paper/2203.00249/) (2022)
- [Tracing a Loose Wordhood for Chinese Input Method Engine](https://mlsanity.com/paper/1712.04158/) (2017)
- [Chinese Pinyin Aided IME, Input What You Have Not Keystroked Yet](https://mlsanity.com/paper/1809.00329/) (2018)
- [PERT: A New Solution to Pinyin to Character Conversion Task](https://mlsanity.com/paper/2205.11737/) (2022)
- [KNPTC: Knowledge and Neural Machine Translation Powered Chinese Pinyin Typo Correction](https://mlsanity.com/paper/1805.00741/) (2018)
- [Real-time Neural-based Input Method](https://mlsanity.com/paper/1810.09309/) (2018)

## Built on by (most cited first, 1 of 1)

- [From Persona to Personalization: A Survey on Role-Playing Language Agents](https://mlsanity.com/paper/2404.18231/) (2024)

## Abstract (arXiv)

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.