Introduction to Small Language Models
Small Language Models (SLMs), such as Microsoft's newly introduced Phi2, are transforming the artificial intelligence landscape. Their streamlined design and smaller size make them more convenient for embedding in various workflows while also lowering computational demands.
Microsoft's Phi2: A Paradigm Shift
These nimble, less resource-intensive models are suitable for numerous applications, facilitating more widespread and sustainable AI use across different sectors. Microsoft's Phi2 is a recent breakthrough in the development of such a model. Despite being a “small-sized” model with 2.7 billion parameters, it pushes back on the idea that only large models can achieve high-level performance. Unveiled at the Microsoft Ignite 2023 event recently, Phi2 marks a significant step in AI's progression, emphasising efficiency, scalability, and ease of access.
SLMs in Legal Technology
The integration of SLMs into legal technology could bring about a transformative shift in how legal services can be enhanced and streamlined. For instance, SLMs could be adeptly used to parse client inquiries, effectively categorising and routing them to the appropriate legal channels. This not only speeds up response times but also ensures that each query is addressed by the most suitable legal expertise.
Advancing Legal Analysis and Impartiality
Additionally, the potential of these models to analyse clauses in contracts and legal documents in a more unbiased manner is groundbreaking. SLMs like Microsoft's Phi2 show notable improvements in reducing toxicity and biases, a critical feature in legal where impartiality is paramount.
Furthermore, the capability of these models to handle a context length of up to 2048 tokens (roughly around 1,500 words) allows for a more thorough and nuanced analysis of legal texts than you would expect in a model you could run on your laptop.
The Case for Energy Efficiency
Whilst Large Language Models (LLMs) are undeniably impressive in their capabilities, it is crucial to consider their energy consumption, especially when deployed for simple tasks. The significant computational power required for these extensive models leads to considerable energy usage, prompting concerns about sustainability and efficiency.
Utilising these more compact models for tasks that do not necessitate the full might of LLMs can strike a balance between technological prowess and environmental stewardship. SLMs offer a viable alternative, ensuring the application of AI in a manner that is both energy-efficient and sustainable, suited to the specific requirements of the task. This approach not only conserves energy but also broadens the potential for AI technology's application and accessibility across legal.
We don't always need to reach for LLMs when SLMs could do the job just as well.