Prompt Ensembles Make LLMs More Reliable

Anyone who has worked with large language models (LLMs) will know that prompt engineering is an informal and difficult process. Small changes to a prompt can cause massive changes to the model’s output, it is difficult (or even impossible in some cases) to know the impact that changing a prompt will have, and prompting behavior is highly dependent on the type of model being used. The fragile nature of prompt engineering is a harsh reality when we think about creating applications with LLMs. If we cannot predict how our model will behave, how can we build a dependable system around this model? Although LLMs are incredibly capable, this problem complicates their use in many practical scenarios. <blockquote> “Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly perfect prompt for a task.” — from [2] </blockquote> Given the fragile nature of LLMs, finding techniques that make these models more accurate and reliable has recently become a popular research topic. In this overview, we will focus on one technique in particular — prompt ensembles. Put simply, prompt ensembles are just sets of diverse prompts that are meant to solve the same problem. To improve LLM reliability, we can generate an answer to a question by querying the LLM with multiple different input prompts and considering each of the model’s responses when inferring a final answer. As we will see, some research on this topic is quite technical. However, the basic idea behind these techniques is simple and can drastically improve LLM performance, making prompt ensembles a go-to approach for improving LLM reliability. <a href="https://towardsdatascience.com/prompt-ensembles-make-llms-more-reliable-ae57ec35b5f7">Website</a>