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Machine Learning Approaches for Principle Prediction in Naturally Occurring Stories
To do this, we extend a dataset that has been previously used to train a binary normative classifier with annotations of moral principles.
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning
However, simply fine-tuning a generative language model with a contrastive reward model does not always reliably result in a story generation system capable of generating stories that meet user preferences.
Automated Story Generation as Question-Answering
Neural language model-based approaches to automated story generation suffer from two important limitations.
Cut the CARP: Fishing for zero-shot story evaluation
Recent advances in large-scale language models (Raffel et al., 2019; Brown et al., 2020) have brought significant qualitative and quantitative improvements in machine-driven text generation.
Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior
As more machine learning agents interact with humans, it is increasingly a prospect that an agent trained to perform a task optimally, using only a measure of task performance as feedback, can violate societal norms for acceptable behavior or cause harm.
Fabula Entropy Indexing: Objective Measures of Story Coherence
Automated story generation remains a difficult area of research because it lacks strong objective measures.
Playing Text-Based Games with Common Sense
We test our technique in the 9to05 game, which is an extreme version of a text based game that requires numerous interactions with common, everyday objects in common, everyday scenarios.
Reducing Non-Normative Text Generation from Language Models
Our normative fine-tuning technique is able to reduce non-normative text by 27-61%, depending on the data set.
Learning Norms from Stories: A Prior for Value Aligned Agents
Value alignment is a property of an intelligent agent indicating that it can only pursue goals and activities that are beneficial to humans.
Improving Deep Reinforcement Learning in Minecraft with Action Advice
We hypothesize that interactive machine learning IML, wherein human teachers play a direct role in training through demonstrations, critique, or action advice, may alleviate agent susceptibility to aliasing.
