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A New Approach to Merging Large Language Models: Exploring Model Kinship

Large language models (LLMs) are revolutionizing the way we interact with technology. However, training LLMs can be expensive and time-consuming. One promising approach to overcome these challenges is model merging, which involves combining multiple models to create a more powerful, generalized model. However, current model merging strategies often rely on trial and error, lacking a principled way to guide the selection of models and predict the expected performance gains.

In a new paper, “Exploring Model Kinship for Merging Large Language Models,” researchers introduce the concept of “model kinship,” a metric that measures the degree of similarity or relatedness between two LLMs. The researchers drew inspiration from biological evolution, where the concept of kinship helps explain the similarities between species. They argue that model kinship can be a useful indicator of the expected performance gains after merging two models.

For example, if two models are very similar in their underlying structure (e.g., trained on similar datasets), their model kinship would be high, and merging them is less likely to result in significant performance improvements. In contrast, merging models with low kinship might lead to better performance gains due to the introduction of new information and capabilities.

The researchers further propose a new model merging strategy called “Top-k Greedy Merging with Model Kinship.” This strategy combines the greedy approach, which selects the top-performing models at each step, with the model kinship criterion to help guide the selection of models. This approach can help mitigate the degradation (local optima) often encountered in model merging, leading to more robust and effective model evolution.

The paper presents a comprehensive empirical analysis of model merging experiments, demonstrating the correlation between model kinship and performance gains. Furthermore, the researchers highlight the importance of understanding the different stages of model evolution, particularly the saturation stage, where performance improvements start to plateau. They suggest that this saturation stage might be due to a convergence in weight space, leading to a decrease in the effectiveness of model merging.

The researchers conclude that model kinship offers a novel perspective on model merging, providing a principled way to guide the selection of models and predict performance gains. Their new model merging strategy can help researchers achieve more robust and efficient model evolution, ultimately leading to more powerful and generalizable LLMs.

This research provides a foundation for developing more sophisticated and effective model merging strategies in the future. It also opens up new avenues for understanding the evolution of LLMs and the relationship between model structure, performance, and task capabilities. As LLMs continue to evolve, it is crucial to develop better strategies for merging models, enabling the development of even more powerful and versatile AI systems.