About Giving GPT-3 a Turing Test

The blog post titled “Giving GPT-3 a Turing Test” by Kevin Lacker explores the capabilities of OpenAI’s GPT-3 language model. The author conducts a series of tests to evaluate how human-like GPT-3’s responses are, and how close it is to passing a Turing test.

Here are four key features highlighted in the blog

  1. Common Sense: GPT-3 demonstrates an ability to answer common sense questions accurately, which is traditionally a challenge for AI systems. However, it struggles with questions that are nonsensical or not commonly discussed.
  2. Trivia Questions: The model performs well with obscure trivia questions, often providing accurate answers. However, it tends to provide incorrect answers to invalid questions instead of expressing uncertainty.
  3. Logic: GPT-3 shows limitations in handling simple math questions and reasoning about sequential operations. It seems to have a limited short-term memory and struggles with reasoning about more than one or two objects in a sentence.
  4. Prompt Dependence: The behavior of GPT-3 can change drastically with different prompts. It’s possible to improve GPT-3’s performance on specific tasks by using a prompt that solves similar problems.