14 citations · 41 across the 14 of their papers we have counts for
7 papers · 1 filter
SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms
Alex Havrilla, Edward Hughes, Mikayel Samvelyan +1
Large language model (LLM) driven synthetic data generation has emerged as a powerful method for improving model reasoning capabilities. However, most methods either distill large…
Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights
Zhaiming Shen, Alex Havrilla, Rongjie Lai +2
Transformers serve as the foundational architecture for large language and video generation models, such as GPT, BERT, SORA and their successors. Empirical studies have demonstrate…
IGDA: Interactive Graph Discovery through Large Language Model Agents
Alex Havrilla, David Alvarez-Melis, Nicolo Fusi
Large language models () have emerged as a powerful method for discovery. Instead of utilizing numerical data, LLMs utilize associated variable $\textit{semantic met…
Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
Alex Havrilla, Andrew Dai, Laura O'Mahony +17
Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct compariso…
Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data
Alex Havrilla, Wenjing Liao
When training deep neural networks, a model's generalization error is often observed to follow a power scaling law dependent both on the model size and the data size. Perhaps the b…
Teaching Large Language Models to Reason with Reinforcement Learning
Alex Havrilla, Yuqing Du, Sharath Chandra Raparthy +6
Reinforcement Learning from Human Feedback (\textbf{RLHF}) has emerged as a dominant approach for aligning LLM outputs with human preferences. Inspired by the success of RLHF, we s…
Understanding the Effect of Noise in LLM Training Data with Algorithmic Chains of Thought
Alex Havrilla, Maia Iyer
During both pretraining and fine-tuning, Large Language Models (\textbf{LLMs}) are trained on trillions of tokens of text of widely varying quality. Both phases of training typical…