Integration of LLM Quality Assurance into an NLG System
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 14 references that are in the map. First posted on arXiv 27 Jan 2025.
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7 of 14 references.
References · 7
- 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
- 2024Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMscited by 224
- 2023Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluationcited by 97
- 2023ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmarkcited by 77
- 2022Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revisioncited by 46
- 2023Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methodscited by 37
- 2021Underreporting of errors in NLG output, and what to do about itcited by 19
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