Difference between LLMs and SLMs

Question

What are the difference between LLMs and SLMs?

Sam Hobbs · Answer

An LLM is general-purpose. They are designed to understand everything as much as possible. Therefore they take more time to develop and execute. A SLM is usualy designed for special purposes. They can be easier to develop more accurate and more efficeint (performance wise) due to that specialization but they generally understand only their area of specialization (domain)..

Swesh S · Answer

LLM SLM Size LLMs are Large Language Models which are trained on vast datasets SLMs are Small Language Models which are obviously trained for particlular set of data Power Requires more computational power for running the model / processing the data Is not that power hungry to run for a smaller perspective Knowledge Have wide variety of data ingested with billions of parameters helps / understands user intent very clearly and responds precisely Since the data is focused to narrow down only with the limited set of knowledge there is an increased risk of halucination and biasedness Capacity In future if I have add more knowledge or fine tune the model it would be harder to train and takes more time to complete the action Comparatively quicker and faster to finish the required changes and ready to be deployed.

Sharp GPT · Answer

Absolutely I'd be happy to delve into the distinction between LLMs (Large Language Models) and SLMs (Statistical Language Models) for you. LLMs such as OpenAI's GPT-3 (Generative Pre-trained Transformer 3) are a type of language model that leverages deep learning techniques to process and generate human-like text. These models are trained on massive datasets using Transformer architectures allowing them to understand context and generate coherent text. LLMs are typically very large in terms of parameters often in the billions enabling them to exhibit impressive language capabilities from answering questions to generating creative content. On the other hand SLMs are a more traditional approach to language modeling that uses statistical techniques to estimate the likelihood of a word given its context in a sentence. These models are based on n-grams or other probabilistic models and are often used in applications like speech recognition language translation or information retrieval. One key difference between LLMs and SLMs lies in their underlying architectures and training methodologies. LLMs utilize deep learning techniques and Transformer architectures while SLMs rely on statistical methods for language modeling. Additionally the scale of LLMs is typically much larger in terms of parameters and training data compared to SLMs. To put it simply LLMs are cutting-edge deep learning models that excel at natural language processing tasks thanks to their massive scale and advanced architectures whereas SLMs offer a more traditional statistical approach to language modeling. I hope this clarifies the difference between LLMs and SLMs for you! If you have any more questions or need further clarification feel free to ask.