Large Language Models (“LLMs”) have vastly caught the attention of the tech world thanks to their potential for Natural Language Processing (“NPL2)). However, the large amount of data and resources required by LLMs have come to overwhelm the general public: companies seeking to leverage AI technology are increasingly finding these models difficult to access, implement, and maintain, and their inclusion in daily operations is proving more a struggle rather than a tool for process optimization.

It is imperative for corporations and individuals to identify their goals and needs regarding AI integrations prior to any AI tool selection or integration process -as useful a tool as LLMs are they also have limitations, alas these models are not always a project’s perfect match.

Here is where Small Language Models (“SLMs”) arise as an alternative to LLMs -and, in some cases, as a better fit for company needs. 

At Renaiss, we believe that leveraging SLMs is key to unlocking the full potential of most enterprise AI use-cases or systems. In fact, the August 2024 State of Intelligent Automation Report by ABBY stated that IT leaders have a higher level of trust in SLMs compared to other models, finding that sectors like manufacturing show the highest trust in SLMs (92%), close to the 91% reflected by financial and IT industries. Furthermore, it is important for companies to understand that larger models are not automatically more useful for a specific purpose -in fact, it is oftentimes the other way around: specific SLMs are better suited to carry out specialized tasks. Here we explain why!

What are SLMs?

As the name indicates, SLMs are smaller versions of their larger counterparts, LLMs. These smaller models are usually characterized by the “fewer” parameters (this is, internal variables) they feature, which often range from a few million to a few hundred million -although this may seem like a lot, LLMs feature an even greater number of parameters, from billions to trillions; for instance, GPT-3 used around 175 billion parameters. The fewer parameters, the less general the model is, but also the less demanding (thus, less expensive) from both the computational and hardware perspective. 

Even with lower computational power, SLMs provide good efficiency, have faster inference times, and require less resources, therefore being (often) more efficient and cost/time effective than LLMs. In addition, they also have high-end accessibility and customization levels, which allows developers and organizations to leverage AI without having to worry about greater resource investments.

The limitations of LLMs 

A pressing matter many organizations and developers will have to tend to in the near future is whether their LLM-based developments and integrations would be most efficient if SLMs were employed instead. The conclusion will depend on whether LLMs’ limitations are impairing projects. Some of the most relevant limitations associated with most LLMs are the following:

Resource efficiency is one of the key challenges LLMs face. Their larger size inevitably requires larger data, infrastructure, processing times, and investment in resources -both human and economic. Smaller models mean fewer resources. SLMs are generally easier to deploy, train, and maintain. Furthermore, SLMs also have faster processing times. As a result, it is not only the development and integration which requires less resources, but also SLMs are less demanding infrastructure-wise as they require less powerful infrastructure -in fact some SLMs can even run on edge devices. In a nutshell, their size and specialization make SLMs cheaper and simpler to deploy and maintain over time without compromising results.

Following the idea above, LLMs are usually more complex, thus more prone to errors, as errors are strongly correlated to complexity -thus, to big volumes of data. On the contrary, SLMs are less likely than LLMs to produce results that are incorrect or irrelevant, hence greatly reducing hallucination risk. 

Compliance and accountability are currently a rather challenging subject in LLM-based integrations or developments. Larger, more general, models lack transparency and traceability regarding data, as well as “explainability”, more often than not. This poses potential compliance issues which may affect companies, especially within regulation-heavy industries (such as healthcare or finance) that require justified answers to evaluate and ensure accountability. 

Likewise, this lack of traceability of the model’s decisions is why it is said that LLMs are sort of “black box” models, which implies they have a particular structure that does not allow for a complete understanding of their internal decision-making processes. This does not only make compliance further complicated but also reduces trust in the model results while making it more difficult for companies or users to correct errors. SLMs are usually easier to interpret due to the fewer parameters they feature. 

There are several other limitations of LLMs, such as reduced security or domain-specific challenges associated with the large amounts of data that LLMs need in their training phase. This lack of specialization makes larger models more prone to errors, as mentioned before, especially when handling technical topics or using terminologies specific to certain domains. 

Advantages of SLMs in corporate environments 

When it comes to resource and energy use, LLMs processes are more in number and complexity than those required by SLMs -illustrated for instance, by Google’s PaLM requirement for a staggering 6,144 TPU v4 chips for its training process. It is a fact that LLMs demand an amount of power and resources that SLMs do not need relating not only training, but also processes such as integration or -as required sometimes by LLMs, manual restarts.

Such a problem is made further evident in the models’ initial training phase. The training phase usually implies dramatic environmental impact because of the carbon footprint associated with LLMs -bigger than SLMs produce. Recent studies highlight how training LLMs such as the ones employed by companies like Meta represents an electrical consumption bigger than what a US family consumes in 40 years -which does not happen with SLMs.

Also highly relevant, in terms of effectiveness -and since they are trained on domain-specific data, SLMs normally have better accuracy compared to their larger counterparts. Contextually speaking, smaller models can compose responses that are usually both more relevant and accurate than those LLMs are able to produce. Such responses are also more traceable and explainable, which contributes to the AI system or integration compliance with applicable laws and regulations.

Also relating to compliance, SLM systems ensure better data privacy. Their data is processed locally or on-premises, which means the data processor has better control over sensitive data or compliance with laws. This has always been a must for regulated industries, but is starting to get increasingly important with the emergence of AI regulations such as the European AI Act.

Customization is another benefit SLMs bring to the table: they offer more flexible, adaptable, features than larger models. Such flexibility enables teams or companies to fine-tune models so as to match their specific goals and requirements, by only dropping customized insights and stakes as applicable to follow business requirements. 

SLMs use cases

• Legal Industry

SLMs can positively impact the legal industries in specific areas such as automating documentation analysis or research. For instance, a widely acknowledged use-case is contract or document review -including suggesting modifications and compliance. Models can also help with document classification, compliance monitoring or document drafting among others.

For each of the aforementioned tasks, SLMs may be a better fit than LLMs: because of their smaller size and variables, these models can easily and effectively be fine-tuned for legal knowledge and functions, without the considerable amount of resources bigger models would require for the same task. Thus, a certain SLM may be fine-tuned to summarize documents, or another might be dexterously trained in compliance regulations. 

• Healthcare

SLMs may also play a big role in improving patient care and operational efficiency in the healthcare industry. Smaller models can be used to assist healthcare organizations and professionals in taking medical notes, suggesting relevant information for treatment based on records, or tracking such patient records. Data privacy is another important use case, since SLMs can help identify sensitive patient information in their records, thus ensuring compliance with any applicable regulations by flagging relevant data whence required.

SLMs are better suited to these healthcare use-cases than LLMs. The efficiency and resource optimization associated with smaller models is a big advantage, and the fact that the healthcare industry involves routine tasks -such as note-taking or coding, makes LLMs too expensive to be sustainable. 

Additionally, they offer an advantage which is decisive in the healthcare industry: better interpretability and transparency, together with better data privacy and security -both of which are crucial for compliance and trust.

The future of SLMs & Renaiss AI

At Renaiss AI we envision SLMs to be the models of the future, especially in enterprise environments. Our Founder & CEO, Javi Martín, has openly spoken about how he believes in a future in which enterprise AI is built on a wide variety of specialized SLMs -instead of on a one-size-fits-all LLM.

Of course, SLMs and AI in general as we know it will continue to evolve exponentially, but there is no denying that the AI of the future must be accurate, cost and time effective and secure, at the very least. SLM-based systems and tools contribute to checking all these boxes for most enterprise AI use-cases -of course, always assuming due infrastructure and implementation of such models. 

Following this vision, we have developed our own production-ready infrastructure with the aim of enabling companies and developers to instrumentalize AI technology. Specifically, our infrastructure already features several specialized SLMs, which enterprises may use at their convenience to create their own specialized agents or optimize their own developments.

If you are leveraging AI systems already (or considering to do so) understand your options first -SLMs might be faster, cheaper, and more accurate than LLMs for a given task. We invite you to contact us if you wish to know more!