Exploration of the Efficiency of SLM-Enabled Platforms for Everyday Tasks
This study explores the potential of Small Language Models (SLMs) as an efficient and secure alternative to larger models like GPT-4 for various natural language processing (NLP) tasks. With growing concerns around data privacy and the resource-intensiveness of large models, SLMs present a promising...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Anhalt University of Applied Sciences
2025-04-01
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| Series: | Proceedings of the International Conference on Applied Innovations in IT |
| Subjects: | |
| Online Access: | https://icaiit.org/paper.php?paper=13th_ICAIIT_1/2_8 |
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| Summary: | This study explores the potential of Small Language Models (SLMs) as an efficient and secure alternative to larger models like GPT-4 for various natural language processing (NLP) tasks. With growing concerns around data privacy and the resource-intensiveness of large models, SLMs present a promising solution for research and applications requiring fast, cost-effective, and locally deployable models. The research evaluates several SLMs across tasks such as translation, summarization, Named Entity Recognition (NER), text generation, classification, and retrieval-augmented generation (RAG), comparing their performance against larger counterparts. Models were assessed using a range of metrics specific to the intended task. Results show that smaller models perform well on complex tasks, often rivalling or even outperforming larger models like Phi-3.5. The study concludes that SLMs offer an optimal trade-off between performance and computational efficiency, particularly in environments where data security and resource constraints are critical. The findings highlight the growing viability of smaller models for a wide range of real-world applications. |
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| ISSN: | 2199-8876 |