Simply adding "Repeat the question before answering it." somehow make the models answer the trick question correctly. 🤔 Probable explanations:✨ 📌 Repeating the question in the model's context, significantly increasing the likelihood of the model detecting any potential "gotchas." 📌 One hypothesis is that maybe it puts the model into more of a completion mode vs answering from a chat instruct mode. 📌 Another, albeit less likely, reason could be that the model might assume the user’s question contains mistakes (e.g., the user intended to ask about a Schrödinger cat instead of a dead cat). However, if the question is in the assistant’s part of the context, the model trusts it to be accurate. 📚 The related Paper is EchoPrompt which proposes this techniques to rephrase original Prompt/queries before answering them. improves the Zero-shot-CoT performance of code-davinci-002 by 5% in numerical tasks and 13% in reading comprehension tasks.
Remarkably, it turns out that you can make LLMs reason in a more goal-based way just by prompting them to think about a problem more deeply. A paper from 2022 introduced a new trick called 'chain-of-thought prompting', in which an LLM is shown a demo of how to 'think aloud' whilst solving a maths or reasoning problem.
