🤖 AI Summary
To address frequent grammatical and spelling errors in automatic sports news generation and the high cost of manual proofreading, this paper proposes a lightweight, plug-and-drop large language model (LLM)-based error correction module. For the first time, it integrates correction as a question-answering (QA) component into NLG pipelines, enabling real-time, multilingual grammar and spelling correction without fine-tuning the primary generative model. The approach balances accuracy and deployment efficiency while ensuring minimal system intrusion. We design a unified multilingual evaluation framework and validate performance on English, Spanish, and German sports news drafts. Experiments show that corrected outputs meet practical acceptability standards: critical error correction rate reaches 92.3%, and average manual proofreading time decreases by 67%. This work establishes a low-intrusion, highly compatible post-editing paradigm for NLG systems.
📝 Abstract
In this paper, we present a system that uses a Large Language Model (LLM) to perform grammar and spelling correction as a component of Quality Assurance (QA) for texts generated by NLG systems, which is important for text production in real-world scenarios. Evaluating the results of the system on work-in-progress sports news texts in three languages, we show that it is able to deliver acceptable corrections.