A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models

TL;DR

This paper introduces a training-free, prompt-free method leveraging large language models for Chinese spelling correction, using minimal distortion and reward strategies to improve accuracy without additional training.

Contribution

The approach is novel in applying LLMs directly for CSC without training or prompts, utilizing a minimal distortion model and reward strategies to enhance performance.

Findings

Significant performance improvements on five datasets

Competitiveness with state-of-the-art CSC models

Effective use of LLMs without training or prompts

Abstract

This work proposes a simple training-free prompt-free approach to leverage large language models (LLMs) for the Chinese spelling correction (CSC) task, which is totally different from all previous CSC approaches. The key idea is to use an LLM as a pure language model in a conventional manner. The LLM goes through the input sentence from the beginning, and at each inference step, produces a distribution over its vocabulary for deciding the next token, given a partial sentence. To ensure that the output sentence remains faithful to the input sentence, we design a minimal distortion model that utilizes pronunciation or shape similarities between the original and replaced characters. Furthermore, we propose two useful reward strategies to address practical challenges specific to the CSC task. Experiments on five public datasets demonstrate that our approach significantly improves LLM performance, enabling them to compete with state-of-the-art domain-general CSC models.