RACQC : Advanced Retrieval-Augmented Generation for C hinese Query Correction (2025)

Abstract: In web search scenarios, erroneous queries frequently degrade users’ experience through irrelevant results, underscoring the pivotal role of Chinese Spelling Check (CSC) systems. Although large language models (LLMs) exhibit remarkable capabilities across many tasks, they face critical challenges in the CSC scenario: (1) poor generalization to rare entities in open-domain searches, and (2) failure to adapt to temporal entity variations due to static parameters, resulting in serious over-correction issues. To tackle this, we present RACQC, a **C**hinese **Q**uery **C**orrection system with **R**etrieval-**A**ugmented Generation(RAG) and multi-task learning. Specifically, our approach (1) integrates dynamic knowledge retrieval through entity-centric RAG to address rare entities and innovatively proposes an entity-title collaborative corpus, and (2) employs contrastive correction tasks to mitigate LLM over-correction tendencies. Furthermore, we propose MDCQC, a **M**ulti-**D**omain **C**hinese **Q**uery **C**orrection benchmark to test the model’s entity correction capabilities. Extensive experiments on several datasets show that RACQC significantly outperforms existing baselines in CSC tasks. Specifically, RACQC achieves a maximum improvement of +9.92% on the search scenario benchmark and +3.2% on the general-domain dataset under the F_1 metric.

## introduction in real-world chinese online search scenarios, users frequently make erroneous queries due to various factors such as input errors and knowledge gaps, resulting in poor relevance of search results. these errors manifest in multiple forms, including homophones, visually similar characters, and omissions or additions of characters. searching with uncorrected queries often leads to substantial discrepancies between search results and users' needs. therefore, a chinese spelling check (csc) system aimed at detecting and correcting spelling errors is significant for search scenarios @xcite . in the csc task, the current mainstream methods based on the sequenceto-sequence(seq2seq) model conceptualize it as a machine translation problem, transforming erroneous sentences into correct ones @xcite . furthermore, as shown in figure 1 , the development of large language models(llms) has further augmented the capabilities of seq2seq models in csc @xcite . however, prior studies have demonstrated that llms do not perform well on csc @xcite . this limitation primarily stems from llms' propensity to over-correct for long-tail or temporal entities @xcite . for instance, as illustrated in figure 1 , a user inputs "handsome sheath" and llm erroneously corrects it into "handsome guard" because it tends to over-correction. the corrected query completely deviates from the user's original search demand, seriously disrupting the user's search experience. ## methods to overcome the limitations outlined above, we propose racqc to augment the capabilities of llms in the csc domain. as illustrated in figure 2 , our training process is divided into two main stages: the first stage employs multi-task training, and the second stage performs supervised fine-tuning (sft) on high-quality samples. additionally, during both the sft and inference stages, we construct a highquality entity-title corpus to enhance the response quality of llms. ## experiments in this section, we present the details of sft and the evaluation results of models on the three csc benchmarks: lemon, mcsc, and our multidomain dataset mdcqc. ## conclusion this paper highlights the over-correction issues llms face in real-world csc scenarios due to their limited error correction capacity and knowledge gap. to address this issue, we propose a novel framework, racqc. it encompasses five different types of training tasks to enhance the model's error correction capability. concurrently, we construct an entity-title corpus to employ the rag methodology to resolve the problem of the model lacking external knowledge. experimental results indicate that racqc achieves sota performance on both search and general datasets.