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Found 14 papers, 8 papers with code
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
We apply preference modeling and reinforcement learning from human feedback (RLHF) to finetune language models to act as helpful and harmless assistants.
Long-Term Benefits for Renewables Integration of Network Boosters for Corrective Grid Security
Using corrective actions to overcome network loading when single lines fail has the potential to free up network capacity that is otherwise underused in preventive N-1 security strategies.
A General Language Assistant as a Laboratory for Alignment
We find that the benefits from modest interventions increase with model size, generalize to a variety of alignment evaluations, and do not compromise the performance of large models.
The strong effect of network resolution on electricity system models with high shares of wind and solar
If we focus on the effect of renewable resource resolution and ignore network restrictions, we find that a higher resolution allows the optimal solution to concentrate wind and solar capacity at sites with better capacity factors and thus reduces system costs by up to 10% compared to a low resolution model.
Physics and Society Computation
Extracting Training Data from Large Language Models
We demonstrate our attack on GPT-2, a language model trained on scrapes of the public Internet, and are able to extract hundreds of verbatim text sequences from the model's training data.
Mitigating heat demand peaks in buildings in a highly renewable European energy system
Space and water heating accounts for about 40% of final energy consumption in the European Union and thus plays a key role in reducing overall costs and greenhouse gas emissions.
Physics and Society
The Near-Optimal Feasible Space of a Renewable Power System Model
Models for long-term investment planning of the power system typically return a single optimal solution per set of cost assumptions.
Physics and Society Systems and Control Systems and Control
Testing Robustness Against Unforeseen Adversaries
Adversaries adapt and evolve their attacks; hence adversarial defenses must be robust to a broad range of unforeseen attacks.
Transfer of Adversarial Robustness Between Perturbation Types
We study the transfer of adversarial robustness of deep neural networks between different perturbation types.
Skill Rating for Generative Models
We explore a new way to evaluate generative models using insights from evaluation of competitive games between human players.
PyPSA-Eur: An Open Optimisation Model of the European Transmission System
PyPSA-Eur, the first open model dataset of the European power system at the transmission network level to cover the full ENTSO-E area, is presented.
Physics and Society
PyPSA: Python for Power System Analysis
In this paper the basic functionality of PyPSA is described, including the formulation of the full power flow equations and the multi-period optimisation of operation and investment with linear power flow equations.
Physics and Society
The role of spatial scale in joint optimisations of generation and transmission for European highly renewable scenarios
The effects of the spatial scale on the results of the optimisation of transmission and generation capacity in Europe are quantified under a 95% CO2 reduction compared to 1990 levels, interpolating between one-node-per-country solutions and many-nodes-per-country.
Physics and Society Applied Physics
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
An adversarial example library for constructing attacks, building defenses, and benchmarking both
