343 citations · 672 across the 16 of their papers we have counts for
20 papers
SkillSmith: Learning to Compose Parametric Skills and Textual Knowledge
Lucio M. Dery, Benedict Aaron Tjandra, Siavash Samiei +4
Agentic systems driven by large language models (LLMs) regularly feature two key mechanisms to autonomously solve complex problems: synthesizing text-based knowledge and procedures…
Context Training with Active Information Seeking
Zeyu Huang, Adhiguna Kuncoro, Qixuan Feng +4
Most existing large language models (LLMs) are expensive to adapt after deployment, especially when a task requires newly produced information or niche domain knowledge. Recent wor…
DiPaCo: Distributed Path Composition
Arthur Douillard, Qixuan Feng, Andrei A. Rusu +7
Progress in machine learning (ML) has been fueled by scaling neural network models. This scaling has been enabled by ever more heroic feats of engineering, necessary for accommodat…
DiLoCo: Distributed Low-Communication Training of Language Models
Arthur Douillard, Qixuan Feng, Andrei A. Rusu +6
Large language models (LLM) have become a critical component in many applications of machine learning. However, standard approaches to training LLM require a large number of tightl…
On "Scientific Debt" in NLP: A Case for More Rigour in Language Model Pre-Training Research
Made Nindyatama Nityasya, Haryo Akbarianto Wibowo, Alham Fikri Aji +4
This evidence-based position paper critiques current research practices within the language model pre-training literature. Despite rapid recent progress afforded by increasingly be…
A Natural Bias for Language Generation Models
Clara Meister, Wojciech Stokowiec, Tiago Pimentel +3
After just a few hundred training updates, a standard probabilistic model for language generation has likely not yet learnt many semantic or syntactic rules of natural language, ma…