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Found 7 papers, 4 papers with code
AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level
First, so far, Hebrew resources for training large language models are not of the same magnitude as their English counterparts.
Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI
In the dynamic landscape of generative NLP, traditional text processing pipelines limit research flexibility and reproducibility, as they are tailored to specific dataset, task, and model combinations.
Efficient Benchmarking of Language Models
The increasing versatility of language models (LMs) has given rise to a new class of benchmarks that comprehensively assess a broad range of capabilities.
Lexical Generalization Improves with Larger Models and Longer Training
While fine-tuned language models perform well on many tasks, they were also shown to rely on superficial surface features such as lexical overlap.
Quality Controlled Paraphrase Generation
Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases.
AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With
Second, there are no accepted tasks and benchmarks to evaluate the progress of Hebrew PLMs on.
