Evolutionary Optimization of Model Merging Recipes (2403.13187v1)

Abstract: We present a novel application of evolutionary algorithms to automate the creation of powerful foundation models. While model merging has emerged as a promising approach for LLM development due to its cost-effectiveness, it currently relies on human intuition and domain knowledge, limiting its potential. Here, we propose an evolutionary approach that overcomes this limitation by automatically discovering effective combinations of diverse open-source models, harnessing their collective intelligence without requiring extensive additional training data or compute. Our approach operates in both parameter space and data flow space, allowing for optimization beyond just the weights of the individual models. This approach even facilitates cross-domain merging, generating models like a Japanese LLM with Math reasoning capabilities. Surprisingly, our Japanese Math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with significantly more parameters, despite not being explicitly trained for such tasks. Furthermore, a culturally-aware Japanese VLM generated through our approach demonstrates its effectiveness in describing Japanese culture-specific content, outperforming previous Japanese VLMs. This work not only contributes new state-of-the-art models back to the open-source community, but also introduces a new paradigm for automated model composition, paving the way for exploring alternative, efficient approaches to foundation model development.

Automated Foundation Model Development through Evolutionary Optimization

Introduction to Evolutionary Model Merging

The development landscape for LLMs has been significantly energized by the advent of model merging techniques. These methodologies amalgamate the capabilities of multiple pre-existing models to forge a composite model that encompasses the strengths of its constituents. This paradigm of model development promises cost-effectiveness by circumventing the need for additional, resource-intensive training phases. However, the effectiveness of model merging hinges on the selection of appropriate source models and their integration strategies—tasks traditionally reliant on human expertise and intuition.

In contrast to this heuristic-based approach, the paper proposes a systematic, evolutionary algorithm-based method for model merging. This method automates the discovery of optimal merging configurations, both in the parameter space and data flow space, to yield foundation models with bespoke capabilities. Through evolutionary optimization, this work transcends the limitations of human intuition, unearthing novel, efficient pathways for model composition that can adaptively harness the distributed intelligence of existing models.

Key Contributions

The paper delineates several pivotal contributions to the domain of foundation model development:

• Automated Model Composition: It presents an evolutionary framework for automatically generating new foundation models through the merger of diverse open-source models. By navigating the combinatorial space in a structured manner, it unlocks the potential to create high-performance foundation models without necessitating extensive additional computational resources.
• Cross-Domain Merging Proficiency: The framework demonstrates a capacity for merging models across disparate domains (e.g., language and mathematics, language and vision), resulting in composite models with enhanced, cross-functional capabilities.
• Benchmark Performance: Application of this method yielded models — a Japanese LLM with Math reasoning capability and a culturally-aware Japanese Vision-LLM (VLM) — that established new benchmarks on various evaluation tasks, underscoring the method's efficacy.
• Generalization Capability: Notably, a 7B parameter LLM surpassed the performance of models with an order of magnitude more parameters on numerous Japanese LLM benchmarks, signaling the approach's exceptional efficiency and generalization ability.
• Impact on Open-Source Community: By contributing state-of-the-art models back to the community, this work not only enhances the public repository of AI tools but also sets a new precedent for collaborative model development.

Evolutionary Optimization: Beyond Intuition in Model Merging

The crux of evolutionary optimization in model merging lies in its dual exploration of parameter space (adjusting model weights) and data flow space (orchestrating the flow of information through model layers). This bifurcated approach permits a comprehensive reconfiguration of model architecture beyond mere weight adjustments, enabling the construction of more potent composite models. The evolutionary process iteratively refines layer assignments and weight configurations, guided by performance metrics specific to the target tasks, gradually converging towards an optimal model architecture.

Implications and Future Directions

Envisaging the future trajectory of AI development, the automation of model merging posits a significant shift towards more resource-efficient methodologies. By paving the way for the speedy generation of specialized foundation models from an expansive pool of pre-trained models, evolutionary optimization positions itself as a linchpin in the drive towards democratized access to cutting-edge AI technologies. Moreover, the concept of cross-domain model merging, facilitated by evolutionary techniques, hints at the untapped potential for creating highly versatile models that transcend conventional domain boundaries.

As we progress, the exploration of evolutionary optimization will invariably extend to other facets of model development, including model parameter selection from a wider pool and the evolution of model swarms with niche capabilities. These advancements herald a new era of AI research, characterized by collaborative, community-driven efforts that leverage collective intelligence to address complex, multifaceted challenges.

In reflection, while the demonstrated approach marks a significant advancement in automated model development, challenges remain, particularly in mitigating logical inconsistencies and ensuring factual accuracy in generated content. Nonetheless, the foundation laid by this work illuminates the path towards a future wherein the evolution of AI is propelled by the synergistic amalgamation of diverse models, fostering a landscape of innovation and discovery.

Conclusion

In sum, this paper posits evolutionary optimization as a transformative tool in the field of LLM development, offering a robust, systematic alternative to intuition-driven model merging. By automating the fusion of diverse capabilities inherent in existing models, it encapsulates a forward-thinking approach to foundation model development, one that promises to accelerate the pace of innovation in AI. As this field continues to evolve, the principles of evolutionary optimization will undoubtedly play a pivotal role in shaping the future of automated, efficient, and collaborative AI research.