Abstract
Large language models have become extremely popular recently due to their ability to achieve strong performance on a variety of tasks, such as text generation and rewriting, but their size and computation cost make them difficult to access, deploy, and secure in many settings. This paper investigates whether small, decoder-only language models can provide an efficient alternative for the tasks of grammar correction and text simplification. The experiments in this paper focus on testing small language models out of the box, fine-tuned, and run sequentially on the JFLEG and ASSET datasets using established metrics. The results show that while SLMs may learn certain behaviors well, their performance remains below strong baselines and current LLMs. The results also show that SLMs struggle with retaining meaning and hallucinations. These findings suggest that despite their efficiency advantages, current SLMs are not yet competitive enough with modern LLMs for rewriting, and further advances in training are required for SLMs to close the performance gap between them and today's LLMs.
Abstract (translated)
近期，大型语言模型由于在文本生成、重写等多种任务上表现出色而变得非常流行。然而，它们的庞大体积和计算成本使得这些模型难以在许多环境中获取、部署和保护。本文研究了小型解码器独享的语言模型是否可以作为语法纠正和文本简化任务的有效替代方案。论文中的实验侧重于测试未经调整、经过微调以及顺序运行的小型语言模型（SLMs）在JFLEG和ASSET数据集上使用公认度量标准下的表现。 结果显示，虽然小型语言模型可能能够很好地学习某些行为，但它们的表现仍然低于强大的基准线和当前的大型语言模型。此外，实验表明，小型语言模型在保留意义和减少幻觉方面存在困难。这些发现表明，尽管小型语言模型具有效率优势，但在文本重写领域目前尚不足以与现代大型语言模型竞争，并且需要进一步的技术进步来缩小它们之间的性能差距。
URL
https://arxiv.org/abs/2601.03874
