The author also highlights the ongoing research and development in the field of SLMs, with the aim of making them more efficient and effective. Despite their smaller size, SLMs are seen as a potential gold rush, with the ability to provide similar capabilities to LLMs but on a more personal and accessible scale. The author concludes by encouraging continued progress in both LLMs and SLMs, emphasizing that both have significant roles to play in the future of AI.
Key takeaways:
- Small Language Models (SLMs) are rising in popularity, designed to work on standalone devices like smartphones without the need for an internet connection, providing potential cost savings and increased privacy.
- SLMs and Large Language Models (LLMs) are not opponents but can coexist, each having its own advantages and disadvantages. LLMs are more detailed and expansive, while SLMs are more compact and efficient.
- There are trade-offs between LLMs and SLMs in terms of performance, accuracy, cost, latency, and user privacy. The choice between the two depends on the specific needs and circumstances of the user.
- Research on SLMs is expanding rapidly, with efforts to make them more efficient and effective. Despite their small size, SLMs have the potential to make a significant impact in areas like on-device AI-based therapy.