3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 3 cited
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…
cs.CL2024★ 1 cited
Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBench
Yotam Perlitz, Ariel Gera, Ofir Arviv +5
Recent advancements in Language Models (LMs) have catalyzed the creation of multiple benchmarks, designed to assess these models' general capabilities. A crucial task, however, is…
cs.CL2024
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…