LLM research: Small Language Models (SLM) are the Future of Agentic AI

We are going to review the different views of Small language models (SLMs) in the world of Agentic AI:
Small Language Models are the Future of Agentic AI
Large language models (LLMs) are often praised for exhibiting near-human performance on a wide range of tasks and valued for their ability to hold a general conversation. The rise of agentic AI systems is, however, ushering in a mass of applications in which language models perform a small number of specialized tasks repetitively and with little variation. Here we lay out the position that small language models (SLMs) are sufficiently powerful, inherently more suitable, and necessarily more economical for many invocations in agentic systems, and are therefore the future of agentic AI. Our argumentation is grounded in the current level of capabilities exhibited by SLMs, the common architectures of agentic systems, and the economy of LM deployment. We further argue that in situations where general-purpose conversational abilities are essential, heterogeneous agentic systems (i.e., agents invoking multiple different models) are the natural choice. We discuss the potential barriers for the adoption of SLMs in agentic systems and outline a general LLM-to-SLM agent conversion algorithm.

Slides for past meetups posted: Github
Recordings have been posted at: YanAITalk

Feel free to reach out if you want to present a paper or a use case at upcoming meetups!

Note: You must have a Zoom account to login (free account is sufficient). Link and password will be shared three days before the meeting.