4 citations · 7 across the 5 of their papers we have counts for
5 papers
Stay Tuned: An Empirical Study of the Impact of Hyperparameters on LLM Tuning in Real-World Applications
Alon Halfon, Shai Gretz, Ofir Arviv +6
Fine-tuning Large Language Models (LLMs) is an effective method to enhance their performance on downstream tasks. However, choosing the appropriate setting of tuning hyperparameter…
Genie: Achieving Human Parity in Content-Grounded Datasets Generation
Asaf Yehudai, Boaz Carmeli, Yosi Mass +5
The lack of high-quality data for content-grounded generation tasks has been identified as a major obstacle to advancing these tasks. To address this gap, we propose Genie, a novel…
Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI
Elron Bandel, Yotam Perlitz, Elad Venezian +9
In the dynamic landscape of generative NLP, traditional text processing pipelines limit research flexibility and reproducibility, as they are tailored to specific dataset, task, an…
Improving Cross-Lingual Transfer through Subtree-Aware Word Reordering
Ofir Arviv, Dmitry Nikolaev, Taelin Karidi +1
Despite the impressive growth of the abilities of multilingual language models, such as XLM-R and mT5, it has been shown that they still face difficulties when tackling typological…
The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers
Ariel Gera, Roni Friedman, Ofir Arviv +4
Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are pre…