It's no surprise that AI doesn't always get things right. Occasionally, it even hallucinates. However, a recent study by Apple researchers has shown even more significant flaws within the mathematical models used by AI for formal reasoning.
As part of the study, Apple scientists asked an AI Large Language Model (LLM) a question, multiple times, in slightly varying ways, and were astounded when they found the LLM offered unexpected variations in the answers. These variations were most prominent when numbers were involved.
Apple's Study Suggests Big Problems With AI's Reliability
The research, published by arxiv.org, concluded there was "significant performance variability across different instantiations of the same question, challenging the reliability of current GSM8K results that rely on single point accuracy metrics." GSM8K is a dataset which includes over 8000 diverse grade-school math questions and answers.
Apple researchers identified the variance in this performance could be as much as 10%. And even slight variations in prompts can cause colossal problems with the reliability of the LLM's answers.
In other words, you might want to fact-check your answers anytime you use something like ChatGPT. That's because, while it may sometimes look like AI is using logic to give you answers to your inquiries, logic isn't what's being used.
AI, instead, relies on pattern recognition to provide responses to prompts. However, the Apple study shows how changing even a few unimportant words can alter that pattern recognition.
One example of the critical variance presented came about through a problem regarding collecting kiwis over several days. Apple researchers conducted a control experiment, then added some inconsequential information about kiwi size.
Both Meta and OpenAI Models Showed Issues
Meta's Llama, and OpenAI's, 01 then altered their answers to the problem from the control despite kiwi size data having no tangible influence on the problem's outcome. OpenAI's GPT-4o also had issues with its performance when introducing tiny variations in the data given to the LLM.
Since LLMs are becoming more prominent in our culture, this news raises a tremendous concern about whether we can trust AI to provide accurate answers to our inquiries. Especially for issues like financial advice. It also reinforces the need to accurately verify the information you receive when using large language models.
That means you'll want to do some critical thinking and due diligence instead of blindly relying on AI. Then again, if you're someone who uses AI regularly, you probably already knew that.