Executive summary:
Small language models (SLMs) are lightweight language models that specialize in specific tasks while using minimal computing resources.
Benefits of SLMs include the following:
SLMs are integral to our cognitive hive AI (CHAI) architecture, where they collaborate with other specialized models to tackle specific tasks with precision. This modular approach boosts efficiency and accuracy across diverse applications, from financial analysis to legal document processing. Contact us to explore how SLMs within CHAI can optimize your business processes.
Small language models are AI systems with fewer parameters and lower computational demands than large language models. They offer faster processing times, lower costs, and enhanced accuracy within their specialized domains.
Small language models (SLMs) represent a specialized subset within the broader field of generative artificial intelligence, specifically for natural language processing (NLP). Characterized by their compact architecture and reduced computational power, SLMs are neural networks containing millions to hundreds of millions of parameters, a fraction of the size of their large language model (LLM) counterparts.
They are a practical choice for environments where efficiency and speed are prioritized over sheer computational power.
According to recent research, task-specific SLMs tend to outperform general-purpose multilingual models, especially in low-resource environments.
Small language models operate by processing text data through neural networks, using a smaller number of parameters to perform specific language-related tasks. These models rely on patterns learned from training data to understand and generate human language.
Despite their compact size, they still follow a structured process to deliver efficient and focused results. Here's an overview of how they work:
The following represent a small sampling of the use case types in which an SLM can provide value:
The following three examples illustrate how SLMs are already making inroads to use cases previously dominated by large language models.
SLMs in healthcare handle medical terminology, procedures, and patient care data. These models are trained on specialized datasets, including medical journals and anonymized patient records, ensuring they can interpret and generate highly accurate information in a healthcare context.
Their applications include summarizing patient records, assisting in diagnostic processes, and staying up-to-date with medical research by summarizing new findings. With a focus on precise medical language and concepts, these models improve decision-making and patient outcomes in clinical settings.
Micro language models (MLMs) are smaller models fine-tuned for customer service tasks. These models are trained on datasets that include customer interactions, FAQs, and product manuals.
By understanding common customer inquiries and company-specific policies, MLMs can provide fast, accurate responses, assist with troubleshooting, and escalate complex issues to human agents when necessary.
For example, an MLM deployed by an IT company could autonomously resolve frequent technical issues. It can allow customer support teams to focus on more complicated requests to improve overall efficiency and customer satisfaction.
An outstanding example of a compact yet powerful SLM is the phi-3-mini model. With 3.8 billion parameters and trained on 3.3 trillion tokens, this model performs on par with larger models such as GPT-3.5 and Mixtral 8x7B.
Despite its small size, phi-3-mini excels in benchmarks such as MMLU, scoring 69%, and MT-bench with a score of 8.38. Its compact nature allows deployment on devices (e.g. smartphones) and is great for applications requiring portability and speed. The model’s dataset, composed of filtered web and synthetic data, ensures high adaptability, safety, and robustness in generating accurate and context-aware responses.
LLMs impress with their broad capabilities, but they're often overkill—or even ineffective—for focused business tasks. SLMs operate faster, cost less, and excel at the specific tasks for which they’ve been trained.
The table below breaks down the differences to help you see which one fits your needs.
| Aspect | SLMs | LLMs |
|---|---|---|
Size and complexity | Fewer parameters; compact architecture | Billions (even hundreds of billions) of parameters; complex architecture |
Performance | Efficient at handling specific, narrow tasks | Handle broad and complex tasks, with deeper contextual understanding |
Computational requirements | Lower compute needed | High computational demands; require powerful GPUs or cloud infrastructure |
Use cases | Domain-specific applications | General-purpose applications |
Cost and resource efficiency | Low cost; optimized for efficiency in resource-constrained environments | High operational cost because of infrastructure and computing needs |
Deployment | Can be deployed on low-power devices (e.g., smartphones, embedded systems) | Primarily deployed on high-performance servers and cloud environments |
In our cognitive hive AI (CHAI) modular architecture, SLMs can be highly focused components that excel in specific tasks. Instead of relying on a single large model, our CHAI leverages multiple, specialized models working together. This collaborative approach leads to more effective outputs, as models can cross-validate each other’s results and ensure higher accuracy.
CHAI doesn’t limit itself to SLMs. Its architecture can incorporate LLMs, large quantitative models, knowledge graphs, and other types of machine learning, IoT, and neural networks. These different components work together like building blocks to create a customized solution for any problem. SLMs play a crucial role in this ecosystem as agile, specialized components that keep the system efficient and adaptable.
Bert is not a small language model. While it is more compact than some of the massive models available today, BERT still contains hundreds of millions of parameters, so is closer to the range of large language models.
Popular examples of small language models include DistilBERT, TinyBERT, and ALBERT. These models compress knowledge from larger models into more compact architectures. MobileBERT and SqueezeNLP also fall into this category and offer efficient language processing for mobile and edge devices with limited resources.
Retrieval-augmented generation (RAG) combines knowledge management and retrieval techniques with language generation to produce more informed responses. An SLM, on the other hand, focuses on performing specific language tasks efficiently with fewer parameters. RAG relies on external data sources, while SLMs work within a more compact framework.
Small language systems (SLS) and SLM serve different purposes. SLMs focus on handling specific language tasks with fewer parameters, while SLSs refer to systems that integrate smaller models and processing approaches. The better choice depends on whether the need is for compact individual models or a system combining multiple smaller tools.
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