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cs.LG2026
Optimal Budgeted Adaptation of Large Language Models
Jing Wang, Jie Shen, Dean Foster +2
The trade-off between labeled data availability and downstream accuracy remains a central challenge in fine-tuning large language models (LLMs). We propose a principled framework f…
cs.LG2024
Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs
Shadi Iskander, Nachshon Cohen, Zohar Karnin +2
Training large language models (LLMs) for external tool usage is a rapidly expanding field, with recent research focusing on generating synthetic data to address the shortage of av…