Small language models are gaining popularity — here’s why

The big picture: Small Language Models (SLMs) are gaining popularity as an alternative to Large Language Models (LLMs), offering potential benefits in efficiency, privacy, and accessibility while LLMs continue to advance.

Key developments in language models:

• LLMs like ChatGPT have demonstrated impressive natural language capabilities by training on vast amounts of online text data
• SLMs aim to provide similar functionality in a more compact form that can run on smartphones and other devices without requiring constant internet access
• Rather than competing, LLMs and SLMs can be seen as complementary approaches suited for different use cases

Comparing LLMs and SLMs:

• LLMs generally offer broader knowledge and capabilities but require powerful servers and internet connectivity
• SLMs are more limited but can run locally on devices, potentially improving speed, privacy, and offline access
• Current SLMs tend to provide less detailed responses than LLMs, though this may change as the technology improves
• Some SLMs allow optional internet access to augment their knowledge when needed

Potential advantages of SLMs:

• Can run entirely on smartphones and other devices without relying on cloud servers
• May reduce costs compared to using expensive cloud infrastructure
• Could offer greater privacy by keeping data local rather than sending it to remote servers
• Faster response times possible due to local processing, though dependent on device capabilities

Ongoing research and development:

• AI researchers are exploring ways to compress LLM capabilities into smaller models
• Techniques developed for SLMs may also help improve the efficiency of LLMs
• Definitions of “small” and “large” models are evolving as technology advances
• Work continues on addressing challenges like potential for bias and hallucination in SLMs

Broader implications: While some view LLMs as the primary path to advanced AI, SLMs demonstrate that “smaller” does not necessarily mean less innovative or impactful. As both approaches continue to progress, they are likely to find complementary roles that leverage their respective strengths in different contexts and applications. The ultimate goal is expanding the utility and accessibility of conversational AI across a range of devices and use cases.