There is ref saying that between 2010-2020 there was a realization that using larger model (than a smaller one) would possibly give higher efficiency and accuracy. What is exactly a smaller and larger model? if someone could explain pls.
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Large Model is the AI model that is trained on large set of data whereas small Model would be that is trained on small amount of data.
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Hi @Vinayu , Thank you for using Discourse. To address your question, let’s distinguish between large and small models:
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Large Model: A large model is trained on an extensive and diverse dataset, often comprising hundreds of gigabytes or even terabytes of data. These models have an enormous number of parameters, often in the billions or trillions as well. This vast capacity allows them to capture intricate details and subtleties within the data. However, this also means they demand substantial computational resources and may be slower during inference times.
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Small Model: In contrast, a small model is trained on a smaller dataset, which could be just a few megabytes in size or gigabytes at most.Consequently, these models have a considerably smaller number of parameters. While this makes them more resource-efficient, it also limits their capacity to capture fine-grained information or context. They tend to provide quicker responses.
For instance, consider ChatGPT as a large model. It’s trained on trillions of tokens and boasts billions or even trillions of parameters. This extensive training results in a broad knowledge base, enabling it to provide insightful responses on a wide array of topics. However, its computational demands may lead to slightly longer response times, typically around a second.
On the other hand, think of a simple chatbot trained on just a single or a few documents. These small models may have at most a few million tokens. While they offer nearly instantaneous responses, they lack a deep understanding of the content within the document. Instead, they rely on patterns and matching within the document, often leading to paraphrased answers in simpler terms.
I hope this answers your query
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The main difference between a large language model (LLM) and a small language model (SLM) is their size and complexity. LLMs are trained on massive datasets of text and code, and have hundreds of billions or even trillions of parameters. SLMs, on the other hand, are trained on smaller datasets and have fewer parameters.
This difference in size and complexity leads to a number of differences in performance and capabilities. LLMs are generally better at tasks that require a deep understanding of language, such as generating creative text formats, translating languages, and answering open ended, challenging, or strange questions in a comprehensive and informative way, even if they are open ended, challenging, or strange. SLMs are typically better at tasks that require less language understanding, such as text classification and sentiment analysis.
Another difference between LLMs and SLMs is their cost and computational requirements. LLMs are very expensive to train and run, and require specialized hardware such as GPUs. SLMs, on the other hand, are much cheaper and less computationally demanding.
Here is a table that summarizes the key differences between LLMs and SLMs:
| Characteristic | LLM | SLM |
|---|---|---|
| Size and complexity | Large, hundreds of billions or even trillions of parameters | Small, fewer parameters |
| Performance and capabilities | Better at tasks that require a deep understanding of language, such as generating creative text formats, translating languages, and answering open ended, challenging, or strange questions in a comprehensive and informative way | Better at tasks that require less language understanding, such as text classification and sentiment analysis |
| Cost and computational requirements | Very expensive to train and run, requires specialized hardware such as GPUs | Much cheaper and less computationally demanding |
Overall, LLMs and SLMs are both powerful tools with different strengths and weaknesses. The best model to use will depend on the specific task at hand.
Note: credit goes to BARD.
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The main difference of the size of model is in the number of parameters.
There are already good answers given by @mtejas12310
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