Model Merging by Uncertainty-Based Gradient Matching

• URL: http://arxiv.org/abs/2310.12808v2
• Date: Fri, 23 Aug 2024 16:25:44 GMT
• Title: Model Merging by Uncertainty-Based Gradient Matching
• Authors: Nico Daheim, Thomas Möllenhoff, Edoardo Maria Ponti, Iryna Gurevych, Mohammad Emtiyaz Khan,
• Abstract summary: We propose a new uncertainty-based scheme to improve the performance by reducing the mismatch. Our new method gives consistent improvements for large language models and vision transformers.
• Score: 70.54580972266096
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail? Here, we connect the inaccuracy of weighted-averaging to mismatches in the gradients and propose a new uncertainty-based scheme to improve the performance by reducing the mismatch. The connection also reveals implicit assumptions in other schemes such as averaging, task arithmetic, and Fisher-weighted averaging. Our new method gives consistent improvements for large language models and vision transformers, both in terms of performance and robustness to hyperparameters. Code available here.

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