95 citations · 136 across the 6 of their papers we have counts for
6 papers
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Stephen Casper, Xander Davies, Claudia Shi +29
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of…
Training Language Models with Language Feedback at Scale
Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak +4
Pretrained language models often generate outputs that are not in line with human preferences, such as harmful text or factually incorrect summaries. Recent work approaches the abo…
Few-shot Adaptation Works with UnpredicTable Data
Jun Shern Chan, Michael Pieler, Jonathan Jao +2
Prior work on language models (LMs) shows that training on a large number of diverse tasks improves few-shot learning (FSL) performance on new tasks. We take this to the extreme, a…
Training Language Models with Language Feedback
Jérémy Scheurer, Jon Ander Campos, Jun Shern Chan +3
Pretrained language models often do not perform tasks in ways that are in line with our preferences, e.g., generating offensive text or factually incorrect summaries. Recent work a…
Instance-wise algorithm configuration with graph neural networks
Romeo Valentin, Claudio Ferrari, Jérémy Scheurer +3
We present our submission for the configuration task of the Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition. The configuration task is to predict a…
Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema
Jérémy Scheurer, Claudio Ferrari, Luis Berenguer Todo Bom +3
Mycosis fungoides (MF) is a rare, potentially life threatening skin disease, which in early stages clinically and histologically strongly resembles Eczema, a very common and benign…