Evolving Prompts In-Context: An Open-ended, Self-replicating Perspective
Authors: name: Jianyu Wang, name: Zhiqiang Hu, name: Lidong Bing

We propose a novel prompt design paradigm that challenges conventional wisdom
in large language model (LLM) prompting. While conventional wisdom prioritizes
well-crafted instructions and demonstrations for in-context learning (ICL), we
show that pruning random demonstrations into seemingly incoherent "gibberish"
can remarkably improve performance across diverse tasks. Notably, the
"gibberish" always matches or surpasses state-of-the-art automatic prompt
optimization techniques, achieving substantial gains regardless of LLM
alignment. Nevertheless, discovering an effective pruning strategy is
non-trivial, as existing attribution methods and prompt compression algorithms
fail to deliver robust results, let alone human intuition. In terms of this, we
propose a self-discover prompt optimization framework, PromptQuine, an
evolutionary search framework that automatically searches for the pruning
strategy by itself using only low-data regimes. Much like the emergent
complexity in nature--such as symbiosis and self-organization--arising in
response to resource constraints, our framework evolves and refines
unconventional yet highly effective prompts by leveraging only the tokens
present within the context. We demonstrate its effectiveness across
classification, multi-choice question answering, generation and math reasoning
tasks across LLMs, while achieving decent runtime efficiency. We hope our
findings can guide mechanistic studies on in-context learning, and provide a
call to action, to pave the way for more open-ended search algorithms for more
effective LLM prompting.