The October 24, 2025 collaboration between many universities have published a paper thst compares the performance of **Large Language Models (LLMs)** and **Small Language Models (SLMs)** on requirements classification tasks within software engineering. Researchers conducted a preliminary study using eight models across three datasets to address concerns about the **high computational cost and privacy risks** associated with using proprietary LLMs. The results indicate that while LLMs achieved an average F1 score only 2% higher than SLMs, this difference was **not statistically significant**, suggesting that SLMs are a **valid and highly competitive alternative**. The study concludes that SLMs offer substantial benefits in terms of **privacy, cost efficiency, and local deployability**, and found that dataset characteristics played a more significant role in performance than did model size.Source:https://arxiv.org/pdf/2510.21443

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