With the above as an overarching perspective, we are ready to jump into today’s discussion.Sometimes you are faced with solving a problem that requires optimization. A famous example is the revered classic known as the traveling salesperson problem. A salesperson is seeking to visit clients or prospective clients in various cities. There is a distance between each city. There are costs associated with traveling from city to city.
“We first showcase OPRO on linear regression and traveling salesman problems, then move on to prompt optimization where the goal is to find instructions that maximize the task accuracy. With a variety of LLMs, we demonstrate that the best prompts optimized by OPRO outperform human designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.”
Any study that examines and explores generative AI is likely to pick some particular generative AI or LLM apps and use those during their study. Experimenters cannot usually try to look at all available generative AI or LLM apps since the research endeavor would be onerous and overly taxing. I mention this to alert you that whenever you hear about research studies on generative AI or LLMs, a prudent thing to discover is what particular apps were used.
Finally, in terms of getting you ready for the big reveal associated with the deep breath prompting aspects, I’d like to briefly highlight what the GSM8K collection or dataset is all about. The GSM8K is a freely available dataset consisting of essentially middle school-level math problems that were created by human problem writers. These math problems are useful for testing purposes, especially for testing AI apps.
“Let’s work together to solve math word problems! First, we will read and discuss the problem together to make sure we understand it. Then, we will work together to find the solution. I will give you hints and help you work through the problem if you get stuck.” Also, note that we have a commingling here in the sense that the “take a deep breath” is combined with the classic passage of thinking step-by-step. We already generally know and agree that asking generative AI work on a step-by-step basis invokes a Chain-of-Thought process and that this alone can make sizable differences in improving problem-solving .
I did some experimentation with ChatGPT on this. Please know that this is ad hoc rather than systematic and robust.
Ai Ai Latest News, Ai Ai Headlines
Similar News:You can also read news stories similar to this one that we have collected from other news sources.
Source: hackernoon - 🏆 532. / 51 Read more »