1 citations · 1 across the 4 of their papers we have counts for
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Will it Merge? On The Causes of Model Mergeability
Adir Rahamim, Asaf Yehudai, Boaz Carmeli +3
Model merging has emerged as a promising technique for combining multiple fine-tuned models into a single multitask model without retraining. However, the factors that determine wh…
Growing a Tail: Increasing Output Diversity in Large Language Models
Michal Shur-Ofry, Bar Horowitz-Amsalem, Adir Rahamim +1
How diverse are the outputs of large language models when diversity is desired? We examine the diversity of responses of several language models to questions with multiple possible…
ContraSim -- Analyzing Neural Representations Based on Contrastive Learning
Adir Rahamim, Yonatan Belinkov
Recent work has compared neural network representations via similarity-based analyses to improve model interpretation. The quality of a similarity measure is typically evaluated by…