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Found 9 papers, 4 papers with code
The Ungrounded Alignment Problem
This paper examines a simplified version of the general problem, where an unsupervised learner is presented with a sequence of images for the characters in a text corpus, and this learner is later evaluated on its ability to recognize specific (possibly rare) sequential patterns.
Better RAG using Relevant Information Gain
A common way to extend the memory of large language models (LLMs) is by retrieval augmented generation (RAG), which inserts text retrieved from a larger memory into an LLM's context window.
Transformer Layers as Painters
We further show that some classes of problems have robustness to skipping layers, running the layers in an order different from how they were trained, or running the layers in parallel.
LaMDA: Language Models for Dialog Applications
We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.
CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions
In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous.
A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors
Domain knowledge can often be encoded in the structure of a network, such as convolutional layers for vision, which has been shown to increase generalization and decrease sample complexity, or the number of samples required for successful learning.
A Growing Long-term Episodic & Semantic Memory
The long-term memory of most connectionist systems lies entirely in the weights of the system.
Conversational Contextual Cues: The Case of Personalization and History for Response Ranking
Unlike previous efforts, which focused on modeling messages and responses, we extend the modeling to long context and participant's history.
Spontaneous Analogy by Piggybacking on a Perceptual System
Using this representation, our system leverages perceptual algorithms to automatically create an ontology of relational structures and to efficiently retrieve analogs for new relational structures from long-term memory.
