Dr. Lance B. Eliot is a world-renowned expert on Artificial Intelligence (AI) with over 7.4+ million amassed views of his AI columns. As a CIO/CTO seasoned executive and high-tech entrepreneur, he combines practical industry experience with deep academic research.
In today’s column, I am continuing my ongoing coverage of prompt engineering strategies and tactics that aid in getting the most out of using generative AI apps such as ChatGPT, GPT-4 , Bard, Gemini, Claude, etc. The focus this time will be on the importance of a technical aspect known as “temperature” and “ temperature settings ” which involve important parameter adjustments that can be made in some generative AI apps.
In the case of generative AI, suppose you ask a question about Abraham Lincoln. If the temperature setting of the AI is currently set at a low range, the odds are that you will get the usual run-of-the-mill response about Lincoln’s life. On the other hand, if the temperature is set relatively high, you are bound to get some unexpected indications about Lincoln that you never knew of.That was a bit of a trick question.
Global settings of temperature are usually of a somewhat neutral basis. The idea is that the temp isn’t too cold nor too hot. It’s the classic Goldilocks principle. This provides a fairly predictable set of outcomes that also allows for a touch of variety. The parlance of the AI field is that predictable AI is known as being deterministic, while less predictable AI is known as being non-deterministic.Lower temps tend toward AI being more so deterministic.
In my classes on prompt engineering, I tell the attendees to think of the temperature as a pot of water. If the pot goes over a temperature of 1, you start to boil or excite the water. This can lead to wild results. If the pot is less than a temperature of 1, you are going to have somewhat calm water and more predictable results.A value of 1 is considered a normal or natural semblance of randomness.
I purposely picked an example that wasn’t a knock-your-socks-off difference. I wanted you to see that the temperature setting does not necessarily cause the world to go berserk. Generative AI is still going to likely provide a response that is generally within the realm of normalcy.A piece of conventional wisdom is that the higher the temperature that you set, the likelier that an AI hallucination will occur.
A tradeoff exists about using high temperatures. You might get surprising results. This could be seemingly creative and awe-inspiring. It might give you new ideas or showcase some potential innovations that heretofore were not necessarily apparent. The results might also be filled with falsehoods. Some of the responses might be utterly incoherent.
Luckily, there is a growing body of research that seeks to empirically explore temperature settings in generative AI. I grandly applaud my fellow AI researchers for tackling the temperature topic. They are doing vital work. Thanks go to them for their heroics. Meanwhile, we all must be mindfully cautious in making any overreaching conclusions. I’ll say this, at least research studies try to do things in a systematic way, which far exceeds those that merely spout temperature-related pronouncements based on the thinnest of speculation and conjecture.
“The temperature parameter of an LLM regulates the amount of randomness, leading to more diverse outputs; therefore, it is often claimed to be the creativity parameter.” This was an empirical study that experimented with a particular generative AI app. Keep that in mind when seeking to generalize the results of the study.
I’ll hit you with one more research study, doing so to whet your appetite and hopefully encourage you to consider reading up on this type of research. Of course, you are equally encouraged to dive into the pool and do research that contributes to this budding area of interest. “We created a multiple-choice question-and-answer exam by randomly sampling problems from standard LLM benchmarks.”
The questions used are typically not aimed at getting creative results. Those are questions that showcase whether generative AI can get the correct result. In that sense, you could suggest that these types of questions are about problem-solving of right-or-wrong outcome questions. There is also a timing factor to be considered. A prompt that worked today might not work the same way tomorrow. Most of the generative AI apps are continually being updated. The updates might alter internal mechanisms that could change how the generative AI reacts to your prompting.I opted to start by asking ChatGPT about the overall nature of temperature settings. We will likely find out which way ChatGPT goes on this matter.
I realize that the ChatGPT response might seem overwhelming in its technical depiction of temperatures and temperature settings.“Sure! Imagine you have a robot friend who tells stories. You can give your robot a special setting called"temperature" to make its stories more predictable or more surprising.”
“For example, with a high temperature, the story about a cat might be:"Once upon a time, there was a cat named Whiskers who found a magical portal in the backyard. Whiskers jumped through and landed in a world where animals could talk and fly. He became friends with a flying turtle and they went on an adventure to find a hidden treasure."
“Using the OpenAI API: When you make an API call to generate text, you can specify the temperature parameter in your request. This allows you to adjust the randomness of the generated text according to your specific needs. Example of an API request with a temperature setting:“Low Temperature : For more focused and deterministic outputs.”“If you are using a platform or service built on ChatGPT that doesn’t expose the temperature setting directly, you might not be able to control it.
“2. **Creative Arts**: Engage in painting, writing, or crafting. Artistic expression can be a therapeutic way to channel anxious energy.”
Large Language Models Llms Generative AI Chatgpt Openai GPT-4 Prompt Engineering Prompting Techniques Temperature Settings Detrministic Non-Deterministic Predictability
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