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cs.CL2025
Reversing Large Language Models for Efficient Training and Fine-Tuning
Eshed Gal, Moshe Eliasof, Javier Turek +3
Large Language Models (LLMs) are known for their expensive and time-consuming training. Thus, oftentimes, LLMs are fine-tuned to address a specific task, given the pretrained weigh…
cs.CL2025
Zero-Shot Detection of LLM-Generated Code via Approximated Task Conditioning
Maor Ashkenazi, Ofir Brenner, Tal Furman Shohet +1
Detecting Large Language Model (LLM)-generated code is a growing challenge with implications for security, intellectual property, and academic integrity. We investigate the role of…