Quoting from @zhanpeng_zhou’s latest work—Cross-Task Linearity Emerges in the Pretraining-Finetuning Paradigm: ‘i) Model averaging takes the average of weights of multiple models, which are finetuned on the same dataset but with different hyperparameter configurations, so as to improve accuracy and robustness. We explain the averaging of weights as the averaging of features at each layer, building a stronger connection between model averaging and logits ensemble than before. ii) Task arithmetic merges the weights of models, that are finetuned on different tasks, via simple arithmetic operations, shaping the behaviour of the resulting model accordingly. We translate the arithmetic operation in the parameter space into the operations in the feature space, yielding a feature-learning explanation for task arithmetic. Furthermore, we delve deeper into the root cause of CTL and underscore the impact of pretraining. We empirically show that the common knowledge acquired from the pretraining stage contributes to the satisfaction of CTL. We also take a primary attempt to prove CTL and find that the emergence of CTL is associated with the flatness of the network landscape and the distance between the weights of two finetuned models. In summary, our work reveals a linear connection between finetuned models, offering significant insights into model merging/editing techniques. This, in turn, advances our understanding of underlying mechanisms of pretraining and finetuning from a feature-centric perspective.’
Quoting from @zhanpeng_zhou’s latest work—Cross-Task Linearity Emerges in the Pretraining-Finetuning Paradigm: ‘i) Model averaging takes the average of weights of multiple models, which are finetuned on the same dataset but with different hyperparameter configurations, so as to improve accuracy and robustness. We explain the averaging of weights as the averaging of features at each layer, building a stronger connection between model averaging and logits ensemble than before. ii) Task arithmetic merges the weights of models, that are finetuned on different tasks, via simple arithmetic operations, shaping the behaviour of the resulting model accordingly. We translate the arithmetic operation in the parameter space into the operations in the feature space, yielding a feature-learning explanation for task arithmetic. Furthermore, we delve deeper into the root cause of CTL and underscore the impact of pretraining. We empirically show that the common knowledge acquired from the pretraining stage contributes to the satisfaction of CTL. We also take a primary attempt to prove CTL and find that the emergence of CTL is associated with the flatness of the network landscape and the distance between the weights of two finetuned models. In summary, our work reveals a linear connection between finetuned models, offering significant insights into model merging/editing techniques. This, in turn, advances our understanding of underlying mechanisms of pretraining and finetuning from a feature-centric perspective.’