From the 1 of 19 linked papers with an AI index.
5 citations · 5 across the 2 of their papers we have counts for
12 papers · 1 filter
Finding Transformer Circuits with Edge Pruning
Adithya Bhaskar, Alexander Wettig, Dan Friedman +1
The path to interpreting a language model often proceeds via analysis of circuits -- sparse computational subgraphs of the model that capture specific aspects of its behavior. Rece…
SimPO: Simple Preference Optimization with a Reference-Free Reward
Yu Meng, Mengzhou Xia, Danqi Chen
Direct Preference Optimization (DPO) is a widely used offline preference optimization algorithm that reparameterizes reward functions in reinforcement learning from human feedback…
MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions
Zexuan Zhong, Zhengxuan Wu, Christopher D. Manning +2
The information stored in large language models (LLMs) falls out of date quickly, and retraining from scratch is often not an option. This has recently given rise to a range of tec…
Lory: Fully Differentiable Mixture-of-Experts for Autoregressive Language Model Pre-training
Zexuan Zhong, Mengzhou Xia, Danqi Chen +1
Mixture-of-experts (MoE) models facilitate efficient scaling; however, training the router network introduces the challenge of optimizing a non-differentiable, discrete objective.…
Language Models as Science Tutors
Alexis Chevalier, Jiayi Geng, Alexander Wettig +19
NLP has recently made exciting progress toward training language models (LMs) with strong scientific problem-solving skills. However, model development has not focused on real-life…
QuRating: Selecting High-Quality Data for Training Language Models
Alexander Wettig, Aatmik Gupta, Saumya Malik +1
Selecting high-quality pre-training data is important for creating capable language models, but existing methods rely on simple heuristics. We introduce QuRating, a method for sele…