Nishat Raihan

Just 2% of programming languages account for nearly 75% of code tokens, revealing a winner-take-all dynamic that should inform how we train and evaluate code-generating LLMs.

Despite their general prowess, open-source LLMs still lag behind proprietary models in the nuanced task of dating texts, even after fine-tuning.