The field of natural language processing is moving towards the development of efficient small language models (SLMs) for specialized applications. These models offer a lightweight, locally deployable alternative to large language models (LLMs), with advantages in privacy, cost, and deployability. Recent studies have shown that SLMs can achieve comparable performance to LLMs in various tasks, such as requirements classification, e-commerce intent recognition, and semantic search. The use of model compression techniques, such as pruning and quantization, has enabled the development of smaller models that can be deployed on edge devices or in resource-constrained environments. Furthermore, the application of directed exoskeleton reasoning and behavioral fine-tuning has been shown to improve the performance of SLMs in factual grounding tasks. Noteworthy papers in this area include: Does Model Size Matter? A Comparison of Small and Large Language Models for Requirements Classification, which found that SLMs can achieve comparable performance to LLMs in requirements classification tasks. Performance Trade-offs of Optimizing Small Language Models for E-Commerce, which demonstrated the viability of optimizing SLMs for e-commerce applications. EdgeRunner 20B: Military Task Parity with GPT-5 while Running on the Edge, which presented a fine-tuned SLM that matches GPT-5 performance on military tasks while running on edge devices.