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20172026
most citedScaling Language Models: Methods, Analysis & Insights from Training Gopher

243 citations · 535 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.LG2025

Watermarking Needs Input Repetition Masking

David Khachaturov, Robert Mullins, Ilia Shumailov +1

Recent advancements in Large Language Models (LLMs) raised concerns over potential misuse, such as for spreading misinformation. In response two counter measures emerged: machine l…

cs.LG2022133 cited

Improving alignment of dialogue agents via targeted human judgements

Amelia Glaese, Nat McAleese, Maja Trębacz +31

We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines. We use reinforcement lear…

cs.LG202014 cited

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming

Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin +8

Convex relaxations have emerged as a promising approach for verifying desirable properties of neural networks like robustness to adversarial perturbations. Widely used Linear Progr…

cs.LG2020

Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification

Daniel J. Mankowitz, Dan A. Calian, Rae Jeong +5

Many real-world physical control systems are required to satisfy constraints upon deployment. Furthermore, real-world systems are often subject to effects such as non-stationarity,…

cs.LG2018

Detecting Adversarial Examples via Neural Fingerprinting

Sumanth Dathathri, Stephan Zheng, Tianwei Yin +2

Deep neural networks are vulnerable to adversarial examples, which dramatically alter model output using small input changes. We propose Neural Fingerprinting, a simple, yet effect…