Chinese spelling correction method based on LLM with multiple inputs

doi:10.11772/j.issn.1001-9081.2024091325

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (3): 849-855.DOI: 10.11772/j.issn.1001-9081.2024091325

• Frontier research and typical applications of large models • Previous Articles Next Articles

Chinese spelling correction method based on LLM with multiple inputs

Can MA¹^,², Ruizhang HUANG¹^,²(), Lina REN¹^,²^,³, Ruina BAI¹^,², Yaoyao WU¹^,²

^1.Engineering Research Center of Ministry of Education for Text Computing and Cognitive Intelligence （Guizhou University），Guiyang Guizhou 550025，China
^2.College of Computer Science and Technology，Guizhou University，Guiyang Guizhou 550025，China
^3.Department of Information Engineering，Guizhou Light Industry Technical College，Guiyang Guizhou 550025，China

Received:2024-09-20 Revised:2024-12-11 Accepted:2024-12-13 Online:2025-02-13 Published:2025-03-10
Contact: Ruizhang HUANG
About author:MA Can， born in 1992， M. S. His research interests include text correction， text mining.
REN Lina， born in 1987， Ph. D.， lecturer. Her research interests include text mining， machine learning.
BAI Ruina， born in 1994， Ph. D.， associate professor. Her research interests include text mining， machine learning.
WU Yaoyao， born in 2000， M. S. candidate. Her research interests include text mining， machine learning.
Supported by:
National Natural Science Foundation of China(62066007);Guizhou Province Science and Technology Support Program(2022277)

基于大语言模型的多输入中文拼写纠错方法

马灿¹^,², 黄瑞章¹^,²(), 任丽娜¹^,²^,³, 白瑞娜¹^,², 伍瑶瑶¹^,²

^1.文本计算与认知智能教育部工程研究中心（贵州大学），贵阳 550025
^2.贵州大学计算机科学与技术学院，贵阳 550025
^3.贵州轻工职业技术学院信息工程系，贵阳 550025

通讯作者: 黄瑞章
作者简介:马灿（1992—），男，湖北鄂州人，硕士，主要研究方向：文本纠错、文本挖掘
任丽娜（1987—），女，辽宁阜新人，讲师，博士，主要研究方向：文本挖掘、机器学习
白瑞娜（1994—），女，新疆乌鲁木齐人，副教授，博士，主要研究方向：文本挖掘、机器学习
伍瑶瑶（2000—），女，贵州安顺人，硕士研究生，主要研究方向：文本挖掘、机器学习。
基金资助:
国家自然科学基金资助项目(62066007);贵州省科技支撑计划项目(2022277)

Abstract

Abstract:

Chinese Spelling Correction （CSC） is an important research task in Natural Language Processing （NLP）. The existing CSC methods based on Large Language Models （LLMs） may generate semantic discrepancies between the corrected results and the original content. Therefore， a CSC method based on LLM with multiple inputs was proposed. The method consists of two stages： multi-input candidate set construction and LLM correction. In the first stage， a multi-input candidate set was constructed using error correction results of several small models. In the second stage， LoRA （Low-Rank Adaptation） was employed to fine-tune the LLM， which means that with the aid of reasoning capabilities of the LLM， sentences without spelling errors were deduced from the multi-input candidate set and used as the final error correction results. Experimental results on the public datasets SIGHAN13， SIGHAN14， SIGHAN15 and revised SIGHAN15 show that the proposed method has the correction F1 value improved by 9.6， 24.9， 27.9， and 34.2 percentage points， respectively， compared to the method Prompt-GEN-1， which generates error correction results directly using an LLM. Compared with the sub-optimal error correction small model， the proposed method has the correction F1 value improved by 1.0， 1.1， 0.4， and 2.4 percentage points， respectively， verifying the proposed method’s ability to enhance the effect of CSC tasks.

Key words: Chinese Spelling Correction (CSC), Large Language Model (LLM), model ensemble, model fine-tuning, prompt learning

摘要：

中文拼写纠错（CSC）是自然语言处理（NLP）中的一项重要研究任务，现有的基于大语言模型（LLM）的CSC方法由于LLM的生成机制，会生成和原文存在语义偏差的纠错结果。因此，提出基于LLM的多输入CSC方法。该方法包含多输入候选集合构建和LLM纠错两阶段：第一阶段将多个小模型的纠错结果构建为多输入候选集合；第二阶段使用LoRA（Low-Rank Adaptation）对LLM进行微调，即借助LLM的推理能力，在多输入候选集合中预测出没有拼写错误的句子作为最终的纠错结果。在公开数据集SIGHAN13、SIGHAN14、SIGHAN15和修正后的SIGHAN15上的实验结果表明，相较于使用LLM直接生成纠错结果的方法Prompt-GEN-1，所提方法的纠错F1值分别提升了9.6、24.9、27.9和34.2个百分点，相较于表现次优的纠错小模型，所提方法的纠错F1值分别提升了1.0、1.1、0.4和2.4个百分点，验证了所提方法能提升CSC任务的效果。

关键词: 中文拼写纠错, 大语言模型, 模型集成, 模型微调, 提示学习

CLC Number:

TP391.1

Can MA, Ruizhang HUANG, Lina REN, Ruina BAI, Yaoyao WU. Chinese spelling correction method based on LLM with multiple inputs[J]. Journal of Computer Applications, 2025, 45(3): 849-855.

马灿, 黄瑞章, 任丽娜, 白瑞娜, 伍瑶瑶. 基于大语言模型的多输入中文拼写纠错方法[J]. 《计算机应用》唯一官方网站, 2025, 45(3): 849-855.

Figures/Tables 10

Fig. 1 Error correction cases of GrammarGPT

Fig. 2 Structure of proposed method

Tab. 1 Prompt templates

S

Prompt-GEN-2

对于待纠错句子： $S$ ，有如下拼写纠错候选结果。

$m . x m$

…

参考候选纠错结果，请对待纠错句子进行拼写纠错，输出纠错后的句子。

Prompt-SEL

下列那个句子没有拼写错误，输出对应句子。若所有候选句子均有错误，则输出无拼写正确结果。

$m . x m$

…

Tab. 1 Prompt templates

提示词名称

提示词模板

Prompt-GEN-1

请对以下句子进行拼写纠错：

S

Prompt-GEN-2

对于待纠错句子： $S$ ，有如下拼写纠错候选结果。

$m . x m$

…

参考候选纠错结果，请对待纠错句子进行拼写纠错，输出纠错后的句子。

Prompt-SEL

下列那个句子没有拼写错误，输出对应句子。若所有候选句子均有错误，则输出无拼写正确结果。

$m . x m$

…

Tab. 2 Statistics of training and test datasets

数据集		句子总数	平均句长	错误数
训练集	SIGHAN13	700	41.8	343
	SIGHAN14	3 437	49.6	5 122
	SIGHAN15	2 338	31.3	3 037
	Wang271K	271 329	42.6	381 962
测试集	SIGHAN13	1 000	74.3	1 224
	SIGHAN14	1 062	50.0	771
	SIGHAN15	1 100	30.6	703
	SIGHAN15-REVISED	1 100	30.6	858

Tab. 3 Spelling error correction effects on SIGHAN datasets

数据集	方法	错误检测				错误纠正
数据集	方法	准确率	精确率	召回率	$F 1$ 值	准确率	精确率	召回率	$F 1$ 值
SIGHAN13	DCN	55.1	57.0	55.3	56.1	54.0	55.8	54.2	55.0
	LEAD	71.2	74.6	71.4	72.9	70.2	73.5	70.3	71.9
	REALISE	69.0	73.2	69.1	71.1	67.6	71.6	67.7	69.6
	Prompt-GEN-1	63.8	71.1	63.1	66.9	60.5	67.3	59.7	63.3
	Prompt-GEN-2	66.7	67.7	66.5	67.1	64.9	65.8	64.7	65.2
	Prompt-SEL	72.3	75.0	72.4	73.7	71.5	74.2	71.6	72.9
SIGHAN14	DCN	69.6	54.3	61.0	57.4	69.2	53.6	60.2	56.7
	LEAD	75.9	63.9	67.5	65.7	75.0	62.3	65.8	64.0
	REALISE	76.2	64.8	70.2	67.4	75.0	62.7	67.9	65.2
	Prompt-GEN-1	54.7	39.1	55.1	45.8	52.2	35.4	49.9	41.4
	Prompt-GEN-2	58.7	43.6	62.7	51.4	57.2	41.4	59.6	48.9
	Prompt-SEL	76.4	64.7	72.3	68.3	75.3	62.8	70.2	66.3
SIGHAN15	DCN	80.5	70.2	77.7	73.8	79.0	67.3	74.6	70.8
	LEAD	85.1	77.1	82.4	79.6	84.3	75.5	80.7	78.0
	REALISE	83.7	76.2	80.9	78.5	83.1	75.0	79.6	77.2
	Prompt-GEN-1	67.2	52.7	64.2	57.9	63.2	46.0	56.0	50.5
	Prompt-GEN-2	71.9	59.5	80.3	68.4	70.5	57.3	77.4	65.8
	Prompt-SEL	85.6	78.2	84.0	81.0	84.3	75.6	81.3	78.4
SIGHAN15-REVISED	DCN	76.4	70.5	68.3	69.4	75.2	68.6	66.5	67.5
	LEAD	76.1	70.1	67.0	68.5	75.5	68.9	65.9	67.3
	REALISE	77.0	71.0	68.8	69.8	76.4	69.8	67.6	68.7
	Prompt-GEN-1	51.6	37.5	48.1	42.1	48.2	32.8	42.0	36.9
	Prompt-GEN-2	71.5	61.5	74.1	67.2	70.0	59.5	71.7	65.0
	Prompt-SEL	77.7	75.3	70.1	72.6	76.9	73.7	68.6	71.1

Tab. 3 Spelling error correction effects on SIGHAN datasets

数据集	方法	错误检测				错误纠正
数据集	方法	准确率	精确率	召回率	$F 1$ 值	准确率	精确率	召回率	$F 1$ 值
SIGHAN13	DCN	55.1	57.0	55.3	56.1	54.0	55.8	54.2	55.0
	LEAD	71.2	74.6	71.4	72.9	70.2	73.5	70.3	71.9
	REALISE	69.0	73.2	69.1	71.1	67.6	71.6	67.7	69.6
	Prompt-GEN-1	63.8	71.1	63.1	66.9	60.5	67.3	59.7	63.3
	Prompt-GEN-2	66.7	67.7	66.5	67.1	64.9	65.8	64.7	65.2
	Prompt-SEL	72.3	75.0	72.4	73.7	71.5	74.2	71.6	72.9
SIGHAN14	DCN	69.6	54.3	61.0	57.4	69.2	53.6	60.2	56.7
	LEAD	75.9	63.9	67.5	65.7	75.0	62.3	65.8	64.0
	REALISE	76.2	64.8	70.2	67.4	75.0	62.7	67.9	65.2
	Prompt-GEN-1	54.7	39.1	55.1	45.8	52.2	35.4	49.9	41.4
	Prompt-GEN-2	58.7	43.6	62.7	51.4	57.2	41.4	59.6	48.9
	Prompt-SEL	76.4	64.7	72.3	68.3	75.3	62.8	70.2	66.3
SIGHAN15	DCN	80.5	70.2	77.7	73.8	79.0	67.3	74.6	70.8
	LEAD	85.1	77.1	82.4	79.6	84.3	75.5	80.7	78.0
	REALISE	83.7	76.2	80.9	78.5	83.1	75.0	79.6	77.2
	Prompt-GEN-1	67.2	52.7	64.2	57.9	63.2	46.0	56.0	50.5
	Prompt-GEN-2	71.9	59.5	80.3	68.4	70.5	57.3	77.4	65.8
	Prompt-SEL	85.6	78.2	84.0	81.0	84.3	75.6	81.3	78.4
SIGHAN15-REVISED	DCN	76.4	70.5	68.3	69.4	75.2	68.6	66.5	67.5
	LEAD	76.1	70.1	67.0	68.5	75.5	68.9	65.9	67.3
	REALISE	77.0	71.0	68.8	69.8	76.4	69.8	67.6	68.7
	Prompt-GEN-1	51.6	37.5	48.1	42.1	48.2	32.8	42.0	36.9
	Prompt-GEN-2	71.5	61.5	74.1	67.2	70.0	59.5	71.7	65.0
	Prompt-SEL	77.7	75.3	70.1	72.6	76.9	73.7	68.6	71.1

Tab. 4 Spelling error correction effects of zero-shot and few-shot learning on SIGHAN15-REVISED dataset

方法	错误检测				错误纠正
方法	准确率	精确率	召回率	$F 1$ 值	准确率	精确率	召回率	$F 1$ 值
文献［15］方法	—	—	—	—	—	62.5	53.4	57.6
Qwen1.5-14B（ZSP）	—	—	—	—	—	—	—	28.1
Qwen1.5-14B（FSP）	—	—	—	—	—	—	—	31.6
Prompt-GEN2（ZSP）	41.4	28.5	40.2	33.3	41.0	28.0	39.5	32.8
Prompt-GEN2（FSP）	47.6	37.3	54.4	44.2	47.6	37.3	54.4	44.2
Prompt-SEL（ZSP）	68.7	69.1	55.3	61.5	68.0	67.7	54.2	60.2
Prompt-SEL（FSP）	78.5	73.6	71.8	72.7	77.8	72.3	70.6	71.4

Tab. 4 Spelling error correction effects of zero-shot and few-shot learning on SIGHAN15-REVISED dataset

方法	错误检测				错误纠正
方法	准确率	精确率	召回率	$F 1$ 值	准确率	精确率	召回率	$F 1$ 值
文献［15］方法	—	—	—	—	—	62.5	53.4	57.6
Qwen1.5-14B（ZSP）	—	—	—	—	—	—	—	28.1
Qwen1.5-14B（FSP）	—	—	—	—	—	—	—	31.6
Prompt-GEN2（ZSP）	41.4	28.5	40.2	33.3	41.0	28.0	39.5	32.8
Prompt-GEN2（FSP）	47.6	37.3	54.4	44.2	47.6	37.3	54.4	44.2
Prompt-SEL（ZSP）	68.7	69.1	55.3	61.5	68.0	67.7	54.2	60.2
Prompt-SEL（FSP）	78.5	73.6	71.8	72.7	77.8	72.3	70.6	71.4

Tab. 5 Character-level statistical analysis of LLM error correction results

统计维度	Prompt-GEN-1	Prompt-GEN-2
长度不一（句子）	88	73
修改字符数	801	887
修改错误字符数	368	300

Tab. 6 Influence of LoRA parameter on model performance on SIGHAN15 dataset

$r$	准确率/%	精确率/%	召回率/%	F1值/%
8	76.2	63.1	60.1	61.6
16	76.5	63.5	60.2	61.8
32	76.6	63.5	60.3	61.8

Tab. 6 Influence of LoRA parameter on model performance on SIGHAN15 dataset

$r$	准确率/%	精确率/%	召回率/%	F1值/%
8	76.2	63.1	60.1	61.6
16	76.5	63.5	60.2	61.8
32	76.6	63.5	60.3	61.8

Tab. 7 Correction case of method based on LLM with multiple inputs

模型	纠错候选句
待纠错句子	要求师公单位对项目进行垫资。
DCN	要求市公单位对项目进行垫资。
LEAD	要求施工单位对项目进行垫资。
REALISE	要求示工单位对项目进行垫资。
Prompt-SEL	要求施工单位对项目进行垫资。

Tab. 8 Failure correction case based on LLM with multiple inputs

模型	纠错候选句
待纠错句子	受到你的邮件，我一遍高兴一边难过。
DCN	收到你的邮件，我一遍高兴一边难过。
LEAD	受到你的邮件，我一边高兴一边难过。
REALISE	受到你的邮件，我一遍高兴一边难过。
Prompt-SEL	无拼写正确结果

References 30

1	DEVLIN J， CHANG M W， LEE K， et al. BERT： pre-training of deep bidirectional Transformers for language understanding ［C］// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics： Human Language Technologies， Volume 1 （Long and Short Papers）. Stroudsburg： ACL， 2019： 4171-4186.
2	CLARK K， LUONG M T， LE Q V， et al. ELECTRA： pre-training text encoders as discriminators rather than generators ［EB/OL］. ［2024-04-13］. .
3	ZHANG S， HUANG H， LIU J， et al. Spelling error correction with Soft-Masked BERT ［C］// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg： ACL， 2020： 882-890.
4	ZHU C， YING Z， ZHANG B， et al. MDCSpell： a multi-task detector-corrector framework for Chinese spelling correction ［C］// Findings of the Association for Computational Linguistics： ACL 2022. Stroudsburg： ACL， 2022： 1244-1253.
5	CHENG X， XU W， CHEN K， et al. SpellGCN： incorporating phonological and visual similarities into language models for Chinese spelling check ［C］// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg： ACL， 2020： 871-881.
6	WANG B， CHE W， WU D， et al. Dynamic connected networks for Chinese spelling check ［C］// Findings of the Association for Computational Linguistics： ACL-IJCNLP 2021. Stroudsburg： ACL， 2021： 2437-2446.
7	XU H D， LI Z， ZHOU Q， et al. Read， listen， and see： leveraging multimodal information helps Chinese spell checking ［C］// Findings of the Association for Computational Linguistics： ACL-IJCNLP 2021. Stroudsburg： ACL， 2021： 716-728.
8	GANAIE M A， HU M， MALIK A K， et al. Ensemble deep learning： a review ［J］. Engineering Applications of Artificial Intelligence， 2022， 115： No.105151.
9	KILICOGLU H， FISZMAN M， ROBERTS K， et al. An ensemble method for spelling correction in consumer health questions ［C］// Proceedings of the 2015 AMIA Annual Symposium. Bethesda， MD： AMIA， 2015： 727-736.
10	TANG C， WU X， WU Y. Are pre-trained language models useful for model ensemble in Chinese grammatical error correction？［C］// Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics （Volume 2： Short Papers）. Stroudsburg： ACL， 2023： 893-901.
11	OUYANG L， WU J， JIANG X， et al. Training language models to follow instructions with human feedback ［C］// Proceedings of the 36th International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2022： 27730-27744.
12	FAN Y， JIANG F， LI P， et al. GrammarGPT： exploring open-source LLMs for native Chinese grammatical error correction with supervised fine-tuning ［C］// Proceedings of the 2023 National CCF Conference on Natural Language Processing and Chinese Computing， LNCS 14304. Cham： Springer， 2023： 69-80.
13	BOROS E， EHRMANN M， ROMANELLO M， et al. Post-correction of historical text transcripts with large language models： an exploratory study ［C］// Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage， Social Sciences， Humanities and Literature. Stroudsburg： ACL， 2024： 133-159.
14	DONG M， CHEN Y， ZHANG M， et al. Rich semantic knowledge enhanced large language models for few-shot Chinese spell checking ［C］// Findings of the Association for Computational Linguistics： ACL 2024. Stroudsburg： ACL， 2024： 7372-7383.
15	ZHOU H， LI Z， ZHANG B， et al. A simple yet effective training-free prompt-free approach to Chinese spelling correction based on large language models ［C］// Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Stroudsburg： ACL， 2024： 17446-17467.
16	QIAO S， OU Y， ZHANG N， et al. Reasoning with language model prompting： a survey ［C］// Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics （Volume 1： Long Papers）. Stroudsburg： ACL， 2023： 5368-5393.
17	HU E J， SHEN Y， WALLIS P， et al. LoRA： low-rank adaptation of large language models ［EB/OL］. ［2024-02-14］. .
18	LIU C L， LAI M H， CHUANG Y H， et al. Visually and phonologically similar characters in incorrect simplified Chinese words ［C］// Proceedings of the 23rd International Conference on Computational Linguistics： Posters Volume. ［S.l.］： Coling 2010 Organizing Committee， 2010： 739-747.
19	LIU S， YANG T， YUE T， et al. PLOME： pre-training with misspelled knowledge for Chinese spelling correction ［C］// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing （Volume 1： Long Papers）. Stroudsburg： ACL， 2021： 2991-3000.
20	ZHANG R， PANG C， ZHANG C， et al. Correcting Chinese spelling errors with phonetic pre-training ［C］// Findings of the Association for Computational Linguistics： ACL-IJCNLP 2021. Stroudsburg： ACL， 2021： 2250-2261.
21	MENG Y， WU W， WANG F， et al. Glyce： glyph-vectors for Chinese character representations ［C］// Proceedings of the 33rd International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2019： 2746-2757.
22	LI Y， MA S， ZHOU Q， et al. Learning from the dictionary： heterogeneous knowledge guided fine-tuning for Chinese spell checking ［C］// Findings of the Association for Computational Linguistics： EMNLP 2022. Stroudsburg： ACL， 2022： 238-249.
23	伍瑶瑶，黄瑞章，白瑞娜，等. 基于对比优化的多输入融合拼写纠错模型［J］. 模式识别与人工智能， 2024， 37（1）：85-94.
	WU Y Y， HUANG R Z， BAI R N， et al. Multi-input fusion spelling error correction model based on contrast optimization ［J］. Pattern Recognition and Artificial Intelligence， 2024， 37（1）：85-94.
24	WANG Y， WANG B， LIU Y， et al. LM-Combiner： a contextual rewriting model for Chinese grammatical error correction ［C］// Proceedings of the 2024 Joint International Conference on Computational Linguistics， Language Resources and Evaluation. ［S.l.］： ELRA and ICCL， 2024： 10675-10685.
25	VASWANI A， SHAZEER N， PARMAR N， et al. Attention is all you need ［C］// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2017： 6000-6010.
26	WU S H， LIU C L， LEE L H. Chinese spelling check evaluation at SIGHAN Bake-off 2013 ［C］// Proceedings of the 7th SIGHAN Workshop on Chinese Language Processing. ［S.l.］： Asian Federation of Natural Language Processing， 2013： 35-42.
27	YU L C， LEE L H， TSENG Y H， et al. Overview of SIGHAN 2014 Bake-off for Chinese spelling check ［C］// Proceedings of the 3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing. Stroudsburg： ACL， 2014： 126-132.
28	TSENG Y H， LEE L H， CHANG L P， et al. Introduction to SIGHAN 2015 Bake-off for Chinese spelling check ［C］// Proceedings of the 8th SIGHAN Workshop on Chinese Language Processing. Stroudsburg： ACL， 2015： 32-37.
29	YANG L， LIU X， LIAO T， et al. Is Chinese Spelling Check ready？ understanding the correction behavior in real-world scenarios ［J］. AI Open， 2023， 4： 183-192.
30	WANG D， SONG Y， LI J， et al. A hybrid approach to automatic corpus generation for Chinese spelling check ［C］// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg： ACL， 2018： 2517-2527.

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Chinese spelling correction method based on LLM with multiple inputs

基于大语言模型的多输入中文拼写纠错方法

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