39 citations · 46 across the 7 of their papers we have counts for
6 papers · 1 filter
: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…
Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification
Yunzhen Feng, Elvis Dohmatob, Pu Yang +2
Large Language Models (LLM) are increasingly trained on data generated by other LLM, either because generated text and images become part of the pre-training corpus, or because syn…
Iteration Head: A Mechanistic Study of Chain-of-Thought
Vivien Cabannes, Charles Arnal, Wassim Bouaziz +3
Chain-of-Thought (CoT) reasoning is known to improve Large Language Models both empirically and in terms of theoretical approximation power. However, our understanding of the inner…
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…
A Tale of Tails: Model Collapse as a Change of Scaling Laws
Elvis Dohmatob, Yunzhen Feng, Pu Yang +2
As AI model size grows, neural scaling laws have become a crucial tool to predict the improvements of large models when increasing capacity and the size of original (human or natur…