CLFEC: A New Task for Unified Linguistic and Factual Error Correction in paragraph-level Chinese Professional Writing

2026-02-27

AI Summary

This work addresses the frequent co-occurrence and mutual influence of linguistic errors (lexical, grammatical, and punctuation) and factual inaccuracies in Chinese professional writing—a challenge inadequately handled by traditional approaches that treat these issues in isolation. To bridge this gap, we introduce the first unified task, CLFEC (Combined Linguistic and Factual Error Correction), and construct a high-quality, multi-domain Chinese dataset spanning politics, finance, law, and medicine. Through systematic exploration of large language model–based paradigms—including prompt engineering, retrieval-augmented generation, and agent-based workflows—we demonstrate that joint in-context correction significantly outperforms decoupled methods. Moreover, when paired with an appropriate base model, the agent-driven workflow further enhances performance, offering both theoretical grounding and practical guidance for industrial-scale, fully automated proofreading systems.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Large Multimodal Models (LMMs)
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Chinese text correction has traditionally focused on spelling and grammar, while factual error correction is usually treated separately. However, in paragraph-level Chinese professional writing, linguistic (word/grammar/punctuation) and factual errors frequently co-occur and interact, making unified correction both necessary and challenging. This paper introduces CLFEC (Chinese Linguistic&Factual Error Correction), a new task for joint linguistic and factual correction. We construct a mixed, multi-domain Chinese professional writing dataset spanning current affairs, finance, law, and medicine. We then conduct a systematic study of LLM-based correction paradigms, from prompting to retrieval-augmented generation (RAG) and agentic workflows. The analysis reveals practical challenges, including limited generalization of specialized correction models, the need for evidence grounding for factual repair, the difficulty of mixed-error paragraphs, and over-correction on clean inputs. Results further show that handling linguistic and factual Error within the same context outperform decoupled processes, and that agentic workflows can be effective with suitable backbone models. Overall, our dataset and empirical findings provide guidance for building reliable, fully automatic proofreading systems in industrial settings.

Problem

• Chinese text correction
• linguistic error
• factual error
• paragraph-level writing
• unified correction

Innovation

• unified error correction
• factual error correction
• Chinese professional writing
• retrieval-augmented generation
• agentic workflows