# A Training-free LLM-based Approach to General Chinese Character Error Correction

arXiv: https://arxiv.org/abs/2502.15266 · HTML: https://mlsanity.com/paper/2502.15266/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Baichuan 2: Open Large-scale Language Models](https://mlsanity.com/paper/2309.10305/) (2023)
- [InternLM2 Technical Report](https://mlsanity.com/paper/2403.17297/) (2024)
- [Spelling Error Correction with Soft-Masked BERT](https://mlsanity.com/paper/2005.07421/) (2020)
- [On the (In)Effectiveness of Large Language Models for Chinese Text Correction](https://mlsanity.com/paper/2307.09007/) (2023)
- [Read, Listen, and See: Leveraging Multimodal Information Helps Chinese Spell Checking](https://mlsanity.com/paper/2105.12306/) (2021)
- [General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining](https://mlsanity.com/paper/2203.10929/) (2022)
- [Rethinking Masked Language Modeling for Chinese Spelling Correction](https://mlsanity.com/paper/2305.17721/) (2023)
- [Chinese Spelling Correction as Rephrasing Language Model](https://mlsanity.com/paper/2308.08796/) (2023)
- [Disentangled Phonetic Representation for Chinese Spelling Correction](https://mlsanity.com/paper/2305.14783/) (2023)
- [CSCD-NS: a Chinese Spelling Check Dataset for Native Speakers](https://mlsanity.com/paper/2211.08788/) (2022)
- [C-LLM: Learn to Check Chinese Spelling Errors Character by Character](https://mlsanity.com/paper/2406.16536/) (2024)
- [MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction](https://mlsanity.com/paper/2210.11720/) (2022)
- [Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking](https://mlsanity.com/paper/2403.08492/) (2024)
- [Overview of CTC 2021: Chinese Text Correction for Native Speakers](https://mlsanity.com/paper/2208.05681/) (2022)

## Built on by (most cited first, 4 of 4)

- [Vision Language Models Are Not (Yet) Spelling Correctors](https://mlsanity.com/paper/2509.17418/) (2025)
- [Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On](https://mlsanity.com/paper/2605.19035/) (2026)
- [CLFEC: A New Task for Unified Linguistic and Factual Error Correction in paragraph-level Chinese Professional Writing](https://mlsanity.com/paper/2602.23845/) (2026)
- [CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards](https://mlsanity.com/paper/2606.00020/) (2026)

## Abstract (arXiv)

Chinese spelling correction (CSC) is a crucial task that aims to correct character errors in Chinese text. While conventional CSC focuses on character substitution errors caused by mistyping, two other common types of character errors, missing and redundant characters, have received less attention. These errors are often excluded from CSC datasets during the annotation process or ignored during evaluation, even when they have been annotated. This issue limits the practicality of the CSC task. To address this issue, we introduce the task of General Chinese Character Error Correction (C2EC), which focuses on all three types of character errors. We construct a high-quality C2EC benchmark by combining and manually verifying data from CCTC and Lemon datasets. We extend the training-free prompt-free CSC method to C2EC by using Levenshtein distance for handling length changes and leveraging an additional prompt-based large language model (LLM) to improve performance. Experiments show that our method enables a 14B-parameter LLM to be on par with models nearly 50 times larger on both conventional CSC and C2EC tasks, without any fine-tuning.