Search Results for author:
Found 11 papers, 2 papers with code
Towards a Model-Theoretic View of Narratives
Our framework affords the ability to discuss key qualities of stories and their communication, including the flow of information from a Narrator to a Reader, the evolution of a Reader’s story model over time, and Reader uncertainty.
Suppressing Pink Elephants with Direct Principle Feedback
Existing methods for controlling language models, such as RLHF and Constitutional AI, involve determining which LLM behaviors are desirable and training them into a language model.
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.
EleutherAI: Going Beyond "Open Science" to "Science in the Open"
Over the past two years, EleutherAI has established itself as a radically novel initiative aimed at both promoting open-source research and conducting research in a transparent, openly accessible and collaborative manner.
Linearly Mapping from Image to Text Space
Prior work has shown that pretrained LMs can be taught to caption images when a vision model's parameters are optimized to encode images in the language space.
VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance
Generating and editing images from open domain text prompts is a challenging task that heretofore has required expensive and specially trained models.
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.
Parameter-Efficient Neural Question Answering Models via Graph-Enriched Document Representations
In this paper, we suggest that large language models are not necessary for good performance by showing a na\"{i}ve implementation of a GCN performs comparably to SoTA models based on pretrained language models.
Towards a Formal Model of Narratives
Our framework affords the ability to discuss key qualities of stories and their communication, including the flow of information from a Narrator to a Reader, the evolution of a Reader's story model over time, and Reader uncertainty.
Fabula Entropy Indexing: Objective Measures of Story Coherence
Automated story generation remains a difficult area of research because it lacks strong objective measures.
