How does APE compare to human-engineered prompts in terms of performance

1. Zero-Shot Performance: The search results indicate that APE-generated prompts can outperform human-engineered prompts on a variety of tasks in a zero-shot setting. For example, on the TruthfulQA benchmark, the APE-generated prompts achieved higher scores on metrics like truthfulness and informativeness compared to the human-crafted prompts [1].

2. Few-Shot In-Context Learning: APE-generated prompts have also been shown to improve the few-shot in-context learning performance of language models compared to human-engineered prompts. The search results mention that APE improved the few-shot performance on 21 out of 24 instruction induction tasks [1].

3. Reasoning and Logical Thinking: One notable example is that APE was able to discover a better general "chain-of-thought" prompt than the human-engineered "Let's think step by step" prompt from prior work. This APE-generated prompt led to improved performance on math reasoning benchmarks like MultiArith and GSM8K [1][3].

4. Breadth of Tasks: The search results indicate that APE-generated prompts were able to match or outperform human prompts on a wide range of tasks, including language understanding, reading comprehension, summarization, and various reasoning tasks from the BIG-Bench benchmark [1].

In summary, the key advantage of APE seems to be its ability to automatically generate prompts that are more effective than human-crafted ones across a diverse set of tasks and benchmarks, both in zero-shot and few-shot settings. The automated prompt engineering approach appears to unlock new capabilities in large language models that surpass what can be achieved through manual prompt design.