Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution
📰 ArXiv cs.AI
Learn to generate diverse LLMs using parameter-efficient neuroevolution and quality-diversity optimization via prompt embedding evolution
Action Steps
- Implement QD-LLM framework using gradient-free optimization to evolve prompt embeddings
- Use prompt embedding evolution to steer generation in frozen LLMs
- Apply quality-diversity optimization to generate diverse LLMs
- Evaluate the performance of QD-LLM using metrics such as mode coverage and diversity
- Compare the results of QD-LLM with other neuroevolution methods to assess its effectiveness
Who Needs to Know This
AI researchers and engineers working on LLM development can benefit from this approach to improve the diversity of their models. This can be particularly useful in teams focused on natural language processing and generative models.
Key Insight
💡 Evolved prompt embeddings can be used to steer generation in frozen LLMs, improving diversity and reducing mode collapse
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🤖 Generate diverse LLMs using parameter-efficient neuroevolution and quality-diversity optimization! 🚀
Key Takeaways
Learn to generate diverse LLMs using parameter-efficient neuroevolution and quality-diversity optimization via prompt embedding evolution
Full Article
Title: Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution
Abstract:
arXiv:2605.09781v1 Announce Type: cross Abstract: Large Language Models exhibit mode collapse, producing homogeneous outputs that fail to explore valid solution spaces. We present QD-LLM, a framework for parameter-efficient neuroevolution that evolves prompt embeddings, compact neural interfaces (~32K parameters) that steer generation in frozen LLMs (70B+ parameters), within a Quality-Diversity (QD) optimization framework. Our contributions: (1) evolved prompt embeddings via gradient-free optimi
Abstract:
arXiv:2605.09781v1 Announce Type: cross Abstract: Large Language Models exhibit mode collapse, producing homogeneous outputs that fail to explore valid solution spaces. We present QD-LLM, a framework for parameter-efficient neuroevolution that evolves prompt embeddings, compact neural interfaces (~32K parameters) that steer generation in frozen LLMs (70B+ parameters), within a Quality-Diversity (QD) optimization framework. Our contributions: (1) evolved prompt embeddings via gradient-free optimi
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