39 citations · 46 across the 7 of their papers we have counts for
3 papers · 2 filters
:Revealing the Decisive Effect of Instruction Diversity on Generalization
Dylan Zhang, Justin Wang, Francois Charton
Understanding and accurately following instructions is critical for large language models (LLMs) to be effective across diverse tasks. In this work, we rigorously examine the key f…
From Symbolic Tasks to Code Generation: Diversification Yields Better Task Performers
Dylan Zhang, Justin Wang, Francois Charton
Instruction tuning -- tuning large language models on instruction-output pairs -- is a promising technique for making models better adapted to the real world. Yet, the key factors…
Instruction Diversity Drives Generalization To Unseen Tasks
Dylan Zhang, Justin Wang, Francois Charton
Instruction tuning -- fine-tuning a large language model (LLM) on pairs of instructions and desired outcomes -- is an approach that enables pre-trained language models to perform r…