5 papers · 1 filter
When Reranking Hurts: Uncertainty-Based Gating for Few-Shot Reranking
Orian Dabod, Amir DN Cohen, Gabriel Stanovsky
Few-shot selection typically assumes that reranking retrieved examples always improves performance. We challenge this view by identifying that the expensive reranking step can in f…
From Benchmarks to Skills: Low-Rank Factors for LLM Evaluation
Aviya Maimon, Amir DN Cohen, Gal Vishne +2
Current evaluations of large language models (LLMs) rely heavily on a growing collection of benchmarks and on aggregate benchmark scores, yet it remains unclear what this compariso…
Dicta-LM 3.0: Advancing The Frontier of Hebrew Sovereign LLMs
Shaltiel Shmidman, Avi Shmidman, Amir DN Cohen +1
Open-weight LLMs have been released by frontier labs; however, sovereign Large Language Models (for languages other than English) remain low in supply yet high in demand. Training…
Diversity Over Quantity: A Lesson From Few Shot Relation Classification
Amir DN Cohen, Shauli Ravfogel, Shaltiel Shmidman +1
In few-shot relation classification (FSRC), models must generalize to novel relations with only a few labeled examples. While much of the recent progress in NLP has focused on scal…
Adapting LLMs to Hebrew: Unveiling DictaLM 2.0 with Enhanced Vocabulary and Instruction Capabilities
Shaltiel Shmidman, Avi Shmidman, Amir DN Cohen +1
Training large language models (LLMs) in low-resource languages such as Hebrew poses unique challenges. In this paper, we introduce DictaLM2.0 and DictaLM2.0-Instruct, two LLMs der…