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Found 5 papers, 1 papers with code
Instruction-Following Evaluation for Large Language Models
One core capability of Large Language Models (LLMs) is to follow natural language instructions.
Ranked #3 on
Instruction Following
on IFEval
Enabling Language Models to Implicitly Learn Self-Improvement
Specifically, we reformulate the training objective of reinforcement learning from human feedback (RLHF) -- instead of maximizing response quality for a given input, we maximize the quality gap of the response conditioned on a reference response.
Generalized Fourier Features for Coordinate-Based Learning of Functions on Manifolds
In this work, we generalize positional encoding with Fourier features to non-Euclidean manifolds.
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness.
Fast Transient Simulation of High-Speed Channels Using Recurrent Neural Network
In this paper, we leverage machine learning methods, to be specific, the recurrent neural network (RNN), to generate black-box macromodels and achieve significant reduction of computation time.
