4 accepted papers
Scaling laws provide a fundamental framework for understanding the performance of Large Language Models (LLMs), yet deriving them requires prohibitively expensive evaluations across thousands of checkpoints or millions of inference samples. To address this, we introduce Item Response Scaling Laws (I…
AI benchmark ecosystems compress rich evaluation data into aggregate leaderboard scores, but these scores contain substantial measurement noise whose sources and magnitudes remain unquantified. Without systematic methods to measure this noise and separate signal from artifact, it is unclear when ben…
Supervised finetuning (SFT) on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities observed in modern large language models (LLMs). However, the annotation efforts required to produce high quality responses for instructions are becoming pr…
Recent advancements in large language models (LLMs) have underscored their importance in the evolution of artificial intelligence. However, despite extensive pretraining on multilingual datasets, available open-sourced LLMs exhibit limited effectiveness in processing Vietnamese. The challenge is exa…