Study Examines Small Language Models' Performance with Agent Paradigms
A new research paper investigates the deployment potential of Small Language Models (SLMs) with under 10 billion parameters, addressing their limitations in knowledge and reasoning. The study, published on arXiv as 2604.19299v1, systematically compares three different paradigms: base models, single agents with tool use, and multi-agent collaborative systems. While large language models face challenges like high computational costs, latency, and privacy concerns, SLMs offer a promising alternative for real-world applications. Existing research has largely focused on improving SLMs through scaling laws or fine-tuning methods, overlooking how agent-based approaches could compensate for their weaknesses. This comprehensive analysis represents the first large-scale examination of open-source models under these specific agent paradigms. The findings reveal that single-agent configurations demonstrate notable performance improvements.
Key facts
- Small Language Models (SLMs) have fewer than 10 billion parameters
- Large language models have substantial computational costs, latency, and privacy risks
- Existing research focuses on scaling laws or fine-tuning strategies for SLMs
- The study examines three paradigms: base model, single agent with tools, multi-agent system
- Agent paradigms include tool use and multi-agent collaboration
- The paper is published on arXiv as 2604.19299v1
- SLMs present a promising alternative to large language models
- The study addresses gaps in research about compensating for SLM weaknesses
Entities
Institutions
- arXiv