Utilizing Small Languages Models over Large language Models depends on various factors such as the Specific task, Computational Resources Available and Delivered Performance.
Examples of Some SLM’s are:
- DistilBERT
- Orca 2
- Phi 2
- BERT Mini, Small, Medium, and Tiny.
- GPT-Neo and GPT-j
- MobileBERT
- T5-small
Examples of LLM’s are:
- GPT-4
- BERT (Large)
- Claude
- Cohere
- Ernie
- Gemini
- Gemma
- GPT-3
- GPT-3.5
- Llama
- Lamda
Ultimately, the choice between small and large language models depends on striking a balance between model performance , computational resources and task requirements.
Let’s delve into the difference between utilizing SLM’s over LLM’s.
Small Language Models (SLM's)
Requires fewer computational resources where there are constraints on memory, processing power or energy consumption.
Small models typically have faster inference times compared to large models which might be preferred.
Small models may perform adequately when there is limited training data available if you have a small dataset, a smaller model might generalize better thereby avoiding overfitting.
Small models are often easier to fine-tune or adapt to specific domains or tasks. if you need to tailor the model to a particular use case or Industry, a smaller model might offer more flexibility.
Large Language Models (LLM's)
LLM’s tend to achieve higher accuracy and better performance on a wide range of natural language processing tasks compared to SLM’s. They can leverage their extensive training data and parameters to produce more accurate predictions and outputs.
LLM’s are better equipped to handle complex language tasks that involve understanding context, ambiguity, and subtitles in language. They can generate more coherent and contextually relevant responses in conversational agents and chatbots.
LLM’s often generalize better on diverse and complex datasets, thanks to their larger capacity to learn from a vast amount of data during training. They can handle a wider range of inputs and tasks with fewer manual adjustments.
LLM’s excel at tasks requiring advance language understanding such as language translation, text generation, question answering and understanding nuanced context. Their larger size enables them to capture more complex patterns and relationships in data.
