Language Model Crossover: Variation through Few-Shot Prompting (2302.12170v3)

Abstract: This paper pursues the insight that LLMs naturally enable an intelligent variation operator similar in spirit to evolutionary crossover. In particular, LLMs of sufficient scale demonstrate in-context learning, i.e. they can learn from associations between a small number of input patterns to generate outputs incorporating such associations (also called few-shot prompting). This ability can be leveraged to form a simple but powerful variation operator, i.e. to prompt a LLM with a few text-based genotypes (such as code, plain-text sentences, or equations), and to parse its corresponding output as those genotypes' offspring. The promise of such LLM crossover (which is simple to implement and can leverage many different open-source LLMs) is that it enables a simple mechanism to evolve semantically-rich text representations (with few domain-specific tweaks), and naturally benefits from current progress in LLMs. Experiments in this paper highlight the versatility of language-model crossover, through evolving binary bit-strings, sentences, equations, text-to-image prompts, and Python code. The conclusion is that LLM crossover is a promising method for evolving genomes representable as text.

• The paper introduces LMX, a novel variation operator that uses few-shot prompting to intelligently recombine text-based genotypes.
• The study demonstrates LMX’s effectiveness across domains such as binary strings, symbolic regression, sentiment modification, image generation, and Python code evolution.
• The paper draws parallels with estimation of distribution algorithms, illustrating LLMs’ potential to model complex, high-dimensional distributions in evolutionary tasks.

LLM Crossover: Variation through Few-Shot Prompting

The paper "LLM Crossover: Variation through Few-Shot Prompting" by Meyerson et al. investigates leveraging LLMs as intelligent variation operators, reminiscent of evolutionary crossover mechanisms, to evolve complex text-represented genotypes. Through few-shot prompting, LLMs can generate outputs that combine aspects of input text sequences, akin to the recombination process in evolutionary algorithms (EAs). This paper illustrates the flexibility and potential of LLM-driven variation in evolving diverse artifacts across various domains, including binary strings, symbolic expressions, textual sentences, text-to-image prompts, and Python code.

Key Contributions

1. Innovative Variation Operator: The research introduces LLM Crossover (LMX), a novel crossover operator based on few-shot prompting capabilities of LLMs that can stimulate intelligent variation. By using a few example genotypes as a prompt, LLMs can suggest new genotypes that naturally blend input characteristics. This mechanism potentially bypasses the need for domain-specific recombination strategies, offering a straightforward implementation that benefits from continual advancements in LLMs.
2. Experiments Across Multiple Domains: The paper showcases LMX's application to a multitude of domains:
   • Binary Strings: Verified that LMX can generate novel, valid offspring from parent genotypes with minimal examples.
   • Symbolic Regression: Demonstrated that LMX evolves mathematical expressions that effectively model datasets, showing performance competitive with established genetic programming techniques.
   • Text Sentiment Modification: Applied LMX to refine natural language sentences, enhancing sentiment while preserving essential content.
   • Image Generation: Utilized LMX to evolve Stable Diffusion prompts, optimizing for specific visual characteristics like color intensity.
   • Python Code Evolution: Engaged LMX in generating Python programs within the Sodarace platform, showcasing its effectiveness in evolving functional code snippets.
3. Theoretical Considerations: The paper draws parallels between LMX and estimation of distribution algorithms (EDAs), where LLMs implicitly model high-quality solutions, constructing probabilistic models from few examples and generating variations in a cohesive manner.

Practical and Theoretical Implications

On the practical side, LMX offers a versatile solution for evolving text-based representations without necessitating intricate, tailor-made genetic operators for each domain. This capacity to generalize across varied domains heralds potential utility in a plethora of applications, particularly where data can be represented textually, such as procedural content generation, adaptive narratives, and automated software evolution.

Theoretically, the paper expands the dialogue in EAs by presenting LLMs as powerful distribution approximators, building on the idea of EDAs. This showcases the potential for LLMs to perform intelligent pattern completion, enhancing the robustness and capacity of evolution-inspired algorithms in capturing complex, high-dimensional distributions.

Prospects for AI Development

The ongoing advancements in LLM capabilities are likely to bolster the efficacy of LMX, with future iterations potentially modeling more intricate associations and enhancing various other aspects of automated generation. This work positions LLMs as integral components of EAs, unlocking possibilities for sophisticated adaptation and optimization tasks across computational and creative disciplines. Furthermore, the ability to encode rich semantic representations of domains within prompts promises new opportunities for synergy between LLMs and EAs, advancing the field of neuroevolution and beyond.

In conclusion, LLM Crossover stands as a promising mechanism that exemplifies how cutting-edge machine learning models can intertwine with classic evolutionary computing paradigms to address complex problem sets, opening up new avenues for research and application in AI.