Papers by Adir Rahamim
Fast Forwarding Low-Rank Training (2024.emnlp-main)
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| Challenge: | Modern optimizers provide a spectacular array of tweaks to stabilize training trajectories and accelerate Stochastic Gradient Descent (SGD). |
| Approach: | They propose a fast-forward approach to accelerate large segments of SGD training . they alternate between Adam SGD for burn-in and accelerating by line search . |
| Outcome: | The proposed approach speeds up training without compromising model performance. |
Will it Merge? On The Causes of Model Mergeability (2026.findings-acl)
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| Challenge: | Model merging has emerged as a promising technique for combining fine-tuned models into a single expert model without retraining. |
| Approach: | They propose a model merging technique that preserves weak model knowledge . they define mergeability as a property of model updates that captures how well they retain trained knowledge when merged with other model updates. |
| Outcome: | The proposed method preserves weak knowledge in the base model. |
Text Augmentation Using Dataset Reconstruction for Low-Resource Classification (2023.findings-acl)
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| Challenge: | Existing methods for text classification use labeled data, but labeles are expensive and difficult to obtain. |
| Approach: | They propose a novel method of data augmentation using the text-generation capabilities of language models. |
| Outcome: | The proposed method improves the current state-of-the-art methods for data augmentation on multi-class datasets. |
ContraSim – Analyzing Neural Representations Based on Contrastive Learning (2024.naacl-long)
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| Challenge: | Existing similarity measures perform mediocrely on standard benchmarks . |
| Approach: | They develop a similarity measure based on contrastive learning that learns a parameterized measure by using both similar and dissimilar examples. |
| Outcome: | The proposed measure achieves much higher accuracy than previous similarity measures . it is more suitable for the analysis of neural networks, revealing new insights . |