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http://dx.doi.org/10.25673/119225
Titel: | Exploration of the Efficiency of SLM-Enabled Platforms for Everyday Tasks |
Autor(en): | Rusinov, Volodymyr Basenko, Nikita |
Körperschaft: | Hochschule Anhalt |
Erscheinungsdatum: | 2025-04-26 |
Umfang: | 1 Online-Ressource (6 Seiten) |
Sprache: | Englisch |
Zusammenfassung: | 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. |
URI: | https://opendata.uni-halle.de//handle/1981185920/121183 http://dx.doi.org/10.25673/119225 |
Open-Access: | ![]() |
Nutzungslizenz: | ![]() |
Enthalten in den Sammlungen: | International Conference on Applied Innovations in IT (ICAIIT) |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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2-8-ICAIIT_2025_13(1).pdf | 902.69 kB | Adobe PDF | ![]() Öffnen/Anzeigen |