Small language models are agile yet powerful alternatives to large language models like ChatGPT. Let's explore how to use SLMs effectively in healthcare, education, and NGOs.
Small language models are more governed and specialized than LLMs.
SLMs have a place in our resource-constrained industries
SLMs hold promise in reducing overhead costs and time-consuming tasks. In healthcare, they can make clinics more efficient while protecting sensitive data. SLMs can level the playing field in education by providing low-income families and municipalities with access to high-quality EdTech services. For NGOs, they could make donations go further and mitigate the shortage of volunteers.
However, SLMs require investment in training, as they operate with limited parameters, which can lead to contextual limitations. For example, the word “lead” can refer to a metal or the verb “to guide”. If an SLM only has access to information about the metal, it may lose the meaning of the verb. Human language is vast and varied, which poses challenges for SLM training. Anticipating user questions and avoiding misunderstandings requires careful engineering, which comes with costs.
However, keeping these limitations in mind, SLMs are more energy efficient and have more manageable safety limits. These models are aimed at efficiently and accurately meeting most business needs at a fraction of the cost of LLMs. It makes sense for SMBs to budget for a compact GenAI as their next best tool in a time of widespread AI.
Source: BairesDev
