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Found 4 papers, 2 papers with code
Propagating Knowledge Updates to LMs Through Distillation
Then, we update the model parameters so that the distribution of the LM (the student) matches the distribution of the LM conditioned on the definition (the teacher) on the transfer set.
Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge
Pre-trained language models (LMs) are used for knowledge intensive tasks like question answering, but their knowledge gets continuously outdated as the world changes.
Self-Programming Artificial Intelligence Using Code-Generating Language Models
Prior work in meta-learning and neural architecture search has led to substantial successes across various task domains, spawning myriad approaches for algorithmically optimizing the design and learning dynamics of deep learning models.
Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning
The placement of charging stations in areas with developing charging infrastructure is a critical component of the future success of electric vehicles (EVs).
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