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What is LLM Finetuning?

Finetuning is the process of taking a pre-trained language model (LLM) and further training it on a specific dataset to adapt it to a particular task or domain. This involves adjusting the model’s weights based on the new data, allowing it to learn patterns and nuances specific to the desired domain.

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Benefits of Finetuning

Finetuning offers several advantages when compared to using the base LLM directly:

  • Improved Performance: By training the model on task-specific data, finetuning can significantly enhance the model’s performance on that task, leading to more accurate and relevant outputs.
  • Customization: Finetuning allows for the customization of the model to better align with specific requirements, such as industry jargon, user preferences, output style and format etc.
  • Efficiency: Once finetuned, the model can generate more precise results without needing extensive context in the input. This means lower token usage, making it faster and cheaper.

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When to Finetune

Consider finetuning a base LLM in the following scenarios:

  • Specialized Tasks: When the task requires domain-specific knowledge or terminology that the base model may not be familiar with.
  • High Accuracy Requirements: When the application demands high accuracy and precision, such as in medical diagnosis, legal document analysis, or financial forecasting.
  • Consistent Output: When consistent and reliable output is crucial, and in-context learning may not provide the necessary stability.

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Prompt engineering vs Few-shot learning vs Finetuning

When leveraging LLMs for specific tasks, you have multiple strategies to improve performance:

  • Prompt Engineering: The practice of crafting precise and optimized input prompts to achieve desired model outputs without modifying the underlying model.
  • Few-shot Learning: This involves providing the LLM with a few examples (input and expectedoutput pairs) along the guidelines in the prompt. One way to think about few-shot (in-context learning) is that it is a form of “reasoning by analogy”.
  • Finetuning: This involves adjusting the LLM’s parameters by training it on a domain-specific or task-specific dataset.

Each strategy has its own trade-offs and is best leveraged at different stages of development (image credit):