GPT models are capable of various tasks, such as content generation, text completion, translation, and question-answering,summarisation, thanks to their abilities to generate coherent and contextually relevant text.
Here we look at the concepts of temperature and top-p sampling in GPT models, illustrating their importance in generating diverse and high-quality text outputs.
LLMs tend to hallucinate because they are designed to produce fluent, coherent text. This
occurs because LLMs have no understanding of the underlying reality that language describes.
LLMs use statistics to generate language that is grammatically and semantically correct within the context of the prompt.
Hallucinations can also occur when there is bad information in the source content.
LLMs rely on a large body of training data that data that can contain noise, errors, biases or inconsistencies.
Temperature and Top-p sampling are two essential parameters that can be tweaked to control the output of GPT models used in various applications like chatbots, content generation, and virtual assistants. As a business user or functional professional, understanding these
parameters can help you get the most relevant responses from GPT models without needing extensive data science knowledge.
Temperature: This parameter determines the creativity and diversity of the text generated by the GPT model. A higher
temperature value (e.g., 1.5) leads to more diverse and creative text, while a lower value (e.g., 0.5) results in more focused and deterministic text.
Top-p Sampling: This parameter maintains a balance between diversity and high-probability words by selecting tokens from the top-p most probable tokens whose collective probability mass is greater than or equal to a threshold p. It helps ensure that the output is both diverse and relevant to the given context.
As a business user, you might need to tweak these parameters to get the desired output quality, depending on the specific use case.
Temperature: If the generated text is too random and lacks coherence, consider lowering the temperature value. If the generated text is too focused and repetitive, consider increasing the temperature value.
Top-p Sampling: If the generated text is too narrow in scope and lacks diversity, consider increasing the probability threshold (p). If the generated text is too diverse and includes irrelevant words, consider decreasing the probability
threshold (p). Most models make Temperature and Top-p available as parameters that can be adjusted. You can start with default values and then adjust them based on the quality of the generated text and the specific requirements of your application. It is essential to use these parameters wisely to get the most relevant responses from GPT models.
