1 citations · 2 across the 2 of their papers we have counts for
7 papers · 1 filter
LLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable Objectives
Luísa Shimabucoro, Sebastian Ruder, Julia Kreutzer +2
The widespread adoption of synthetic data raises new questions about how models generating the data can influence other large language models (LLMs) via distilled data. To start, o…
The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm
Aakanksha, Arash Ahmadian, Beyza Ermis +4
A key concern with the concept of "alignment" is the implicit question of "alignment to what?". AI systems are increasingly used across the world, yet safety alignment is often foc…
RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs
John Dang, Arash Ahmadian, Kelly Marchisio +3
Preference optimization techniques have become a standard final stage for training state-of-art large language models (LLMs). However, despite widespread adoption, the vast majorit…
Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruning
Everlyn Asiko Chimoto, Jay Gala, Orevaoghene Ahia +3
Neural Machine Translation models are extremely data and compute-hungry. However, not all data points contribute equally to model training and generalization. Data pruning to remov…
Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model
Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong +14
Recent breakthroughs in large language models (LLMs) have centered around a handful of data-rich languages. What does it take to broaden access to breakthroughs beyond first-class…
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
Shivalika Singh, Freddie Vargus, Daniel Dsouza +30
Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to t…