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This survey provides a comprehensive overview of model merging methods and theories, their applications in various domains and settings, and future research directions. Specifically, we first …
Jun 17, 2024 · In the era of large language models, model merging is a promising way to combine multiple task-specific models into a single multitask model without extra training. However, two …
Ours can be used a plug-and-play approach to enhance OOD generalization of most of model merging methods due to its flexibility. Extensive experiments prove that ours effectively …
Our extensive experiments reveal that model merging tends to perform better on stronger base mod-els, and techniques such as scaling coeficient tuning and sparsification help preserve …
However, a new method known as ‘ Model Merging ’ has been introduced due to increased demand for specialized models in the market. Model merging entails merging several machine-learning …
Opting for a lower improves the privacy (risk of memorization) of outputs at the expense of utility (text-alignment). It requires more sub-stantial optimization of the input prompt, aiming to …
In addition to removing the tail parameters with the smallest weight, the Model Breadcrumbs [30] highlight the importance of removing the parameters (outliers) with the largest weights to further …
Jul 3, 2024 · Memorization in deep learning models occurs when the model overfits to the training data, learning specific details and noise rather than general patterns. This leads to poor …
ABSTRACT This paper investigates the linear merging of models in the context of continual learning (CL). Us-ing controlled visual cues in computer vision experiments, we demonstrate that merging …
This paper proposes a two-stage framework combining robust supervised fine-tuning (R-SFT) with model merging for knowledge editing. Our method first fine-tunes the LLM to internalize new …
Jun 6, 2025 · That opens the door to generalization: the process by which models extract reusable patterns instead of storing every instance. This study didn’t just quantify memorization. It also …
May 21, 2025 · Certifying the IID generalisation ability of deep networks is the first of many requirements for trusting AI in high-stakes applications from medicine to security. However, …
Last, our findings suggest an exciting avenue for model fusion as a tool for mitigating memorization and preserving privacy. By comparing memorization before and after fusion, we demonstrate …
Jun 24, 2025 · Abstract: Model merging, which combines multiple models into a single model, has gained increasing popularity in recent years. By efficiently integrating the capabilities of various …
fine-tuning often leads to catastrophic forgetting, where improvements on a target domain degrade generalization on other tasks, and (ii) merging fine-tuned checkpoints from disparate tasks can …
PriME is an evolutionary approach to LLM personalization that optimizes model merging recipes to create a personalized, parameter-efficient fine-tuning module. Using gradient-free optimization, …
Oct 4, 2024 · Model merging aims to combine multiple expert models into a more capable single model, offering benefits such as reduced storage and serving costs, improved generalization, …
Mar 12, 2025 · This makes traditional multi-task learning based on shared training data impractical. In scenarios where direct access to training data is infeasible, merging model parameters to …
Abstract Model merging aims to combine multiple expert models into a more capable single model, offering benefits such as reduced storage and serving costs, improved generalization, and …
By exploring the potential of model fusion to reduce memorization, we aim to address privacy concerns. 6.1 Method To investigate this, we ne-tune GPT-2 models on different datasets, …
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