When to use few shot leaning for LLMs

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Are you wondering what few shot learning is? Or maybe you want to learn more about when few shot learning should be used for LLMs? Well then you are in the right place! In this article, we tell you everything you need to know to determine whether to use few shot learning for LLMs.

We start out by discussing what few shot learning is and what type of data is required for few shot learning. After that we discuss some of the main advantages and disadvantages of few shot learning. This provides useful context that will add flavor to our discussion of when to use few shot learning for LLMs. Finally, we provide examples of situations where it does and does not make sense to use few shot learning for LLMs.

What is few shot learning?

What is few shot learning for LLMs? In general, few shot learning is when you feed a model a few examples of what a good response to a given prompt looks like before you ask it to respond to other prompts. This shows the model what a good response looks like for a particular prompt and can help the model understand details and caveats about how it should respond to certain prompts.

And how do you provide an LLM with examples of what a good response looks like? You can provide an LLM with examples of what a good response looks like by incorporating examples of prompt and response pairs into the prompt that you feed to the model. You should make sure that the responses you provide are formatted the same way that you want the models responses to be formatted.

What data is needed for few shot learning?

What data is needed for few shot learning? In general, few shot learning is not a technique that requires access to a large amount of data. The only data that you need in order to implement few shot learning is the examples of prompt and response pairs that you want to incorporate into your prompt.

Advantages and disadvantages of few shot learning

What are some of the main advantages and disadvantages of few shot learning for LLMs? In this section, we will summarize some of the main advantages and disadvantages of few shot learning.

Advantages of few shot learning

What are some of the main advantages of few shot learning compared to other techniques that can be applied to improve the performance of LLMs? In this section, we will discuss some of the main advantages of few shot learning.

  • Can help with highly specific output formats. Few shot learning is a very useful technique for situations where it is imperative that your output match a specific format. Showing the model specific examples of what this format looks like and how to translate other prompts into this format can be essential to ensuring that the model uses the appropriate output format.
  • Easy to implement. One of the main advantages of few shot learning is that it is easy to implement. This technique can be implemented in a matter of minutes if you are in a situation where you have access to a few examples of what an input and output should look like for your task.
  • Low latency. Another advantage of few show learning is that it does not add much latency to your system. There may be a small amount of latency added when you increase the size of the prompt you are feeding into the model, but you are not adding any additional calls to the model or lookups that need to be done before an answer can be returned to the user.
  • Improves predictive performance. Few shot learning is a simple method that you can try to improve the predictive power of your model. While it may work better in some cases than others, you have not wasted much time even if you do not see noticeable gains.
  • Can be applied to any model. Another advantage of few shot learning is that it can be applied to all different types of models. That means that it does not limit what types of models or vendors you can work with.
  • Does not introduce privacy concerns. Another advantage of few shot learning is that you are only exposing a small amount of internal data to the model, so the risk of introducing sensitive information such as personally identifiable information (pii) to the model unknowingly is very small.
  • Does not require large computational resources. You do not have to train a model or fine tune a model in order to utilize this technique. That means that you do not need to have access to large computational reassures in order to use this technique.
  • Does not affect guardrails. Another advantage of few shot learning is that since you are not training or fine tuning a model of your own, you will not negatively impact any guardrails that are baked into the model to ensure that it behaves appropriately. Some third party vendors put these types of models in place for foundation models, but remove them when you fine tune a model.

Disadvantages of few shot learning

What are some of the main disadvantages of few shot learning? Here are a few examples of disadvantages of few shot learning for LLMs.

  • Cannot provide domain specific context. One of the main disadvantages of few shot learning for LLMs is that it does not provide an avenue to provide the model with domain specific context that it needs in order to respond to prompts appropriately. If your model is lacking crucial context that it needs in order to operate effectively, you are better off looking into other techniques that can help you incorporate this context.
  • Needs more tokens for prompting. Another disadvantage of few shot learning is that it requires you to use many more tokens in your prompts. This reduces the amount of external information that you have room for and also has the potential to increase your costs. This is especially true if you are using a third party vendor that charges you based on the number of tokens being fed into a model.

When to use few shot learning for LLMs

When does it make sense to use few shot learning for LLMs? In this section, we will provide a few examples of situations where it makes sense to use few shot learning to improve your LLMs.

  • When the model is performing a very specific task. In general, few shot learning is most effective when the model is going to be performing a very specific task. If you are aiming to build a more general system that can address a range of problems, then few shot learning might not be as effective. In these situations, you may not be able to provide enough context on all of the different types of situations that the model should be able to address.
  • When you have a complicated output format. Another of the best examples of a situation where it makes sense to use few shot learning to improve LLM responses is when you want the response of the model to be formatted in a way that is complicated or atypical. In these scenarios, the model can benefit from seeing a few examples of what a properly formatted output looks like.

When not to use few shot learning for LLMs

When is it not a good idea to use full shot learning? In this section, we will describe some examples of instances where it does not make sense to use few shot learning. In these situations, you are generally better off turning to other techniques that can be used to improve LLMs.

  • When your prompts or output are already very long. In order to enable few shot learning, you need to feed multiple examples of what a good prompt and response look like to your model. This reduces the amount of tokens that you will be able to feed to the model for other purposes, such as the number of tokens that will be available for the meat of the prompt itself or the number of tokens that will be available to provide needed context to the model. If you have already allocated a lot of tokens in your prompts to other tasks, or if you have very large outputs that take a large amount of tokens, then you may not be able to spare the tokens required for few shot learning. In these cases, you may be better off turning to fine tuning to teach the model how to execute on a specific task.
  • When the model needs domain specific context. One example of a situation where it does not necessarily make sense to turn to few shot learning is when your model is struggling because it does not have enough context on the domain or application area you are asking to to operate in. In these scenarios, you should use a technique that allows you to provide more context to your model such as fine tuning or retrieval augmented generation. Providing a few examples of good responses will not be sufficient here.

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