3 papers
cs.CL2026
Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness
Tomer Ashuach, Shai Gretz, Yoav Katz +2
Humans use introspection to evaluate their understanding through private internal states inaccessible to external observers. We investigate whether large language models possess si…
cs.CL2024
Conversational Prompt Engineering
Liat Ein-Dor, Orith Toledo-Ronen, Artem Spector +5
Prompts are how humans communicate with LLMs. Informative prompts are essential for guiding LLMs to produce the desired output. However, prompt engineering is often tedious and tim…
cs.LG2024
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…