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Found 7 papers, 2 papers with code
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Neural NLP models are increasingly accurate but are imperfect and opaque---they break in counterintuitive ways and leave end users puzzled at their behavior.
Generative Modeling for Atmospheric Convection
While cloud-resolving models can explicitly simulate the details of small-scale storm formation and morphology, these details are often ignored by climate models for lack of computational resources.
Differentially Private Language Models Benefit from Public Pre-training
Language modeling is a keystone task in natural language processing.
Gradient-based Analysis of NLP Models is Manipulable
Gradient-based analysis methods, such as saliency map visualizations and adversarial input perturbations, have found widespread use in interpreting neural NLP models due to their simplicity, flexibility, and most importantly, their faithfulness.
Multi-Stage Episodic Control for Strategic Exploration in Text Games
Text adventure games present unique challenges to reinforcement learning methods due to their combinatorially large action spaces and sparse rewards.
Scaling Laws for Imitation Learning in Single-Agent Games
Inspired by recent work in Natural Language Processing (NLP) where "scaling up" has resulted in increasingly more capable LLMs, we investigate whether carefully scaling up model and data size can bring similar improvements in the imitation learning setting for single-agent games.
Language-Guided World Models: A Model-Based Approach to AI Control
Installing probabilistic world models into artificial agents opens an efficient channel for humans to communicate with and control these agents.
