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Found 14 papers, 1 papers with code
What Can a Generative Language Model Answer About a Passage?
Generative language models trained on large, diverse corpora can answer questions about a passage by generating the most likely continuation of the passage followed by a question/answer pair.
The Search for Agreement on Logical Fallacy Annotation of an Infodemic
We evaluate an annotation schema for labeling logical fallacy types, originally developed for a crowd-sourcing annotation paradigm, now using an annotation paradigm of two trained linguist annotators.
Towards a Holodeck-style Simulation Game
We introduce Infinitia, a simulation game system that uses generative image and language models at play time to reshape all aspects of the setting and NPCs based on a short description from the player, in a way similar to how settings are created on the fictional Holodeck.
Gluing Neural Networks Symbolically Through Hyperdimensional Computing
This process can be performed iteratively and even on single neural networks by instead making a consensus of multiple classification hypervectors.
Representing Sets as Summed Semantic Vectors
Representing meaning in the form of high dimensional vectors is a common and powerful tool in biologically inspired architectures.
A Computational Theory for Life-Long Learning of Semantics
Semantic vectors are learned from data to express semantic relationships between elements of information, for the purpose of solving and informing downstream tasks.
Deductive and Analogical Reasoning on a Semantically Embedded Knowledge Graph
Representing knowledge as high-dimensional vectors in a continuous semantic vector space can help overcome the brittleness and incompleteness of traditional knowledge bases.
Graphcut Texture Synthesis for Single-Image Superresolution
Texture synthesis has proven successful at imitating a wide variety of textures.
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
We propose to counter these language priors for the task of Visual Question Answering (VQA) and make vision (the V in VQA) matter!
Using a Distributional Semantic Vector Space with a Knowledge Base for Reasoning in Uncertain Conditions
The inherent inflexibility and incompleteness of commonsense knowledge bases (KB) has limited their usefulness.
Yin and Yang: Balancing and Answering Binary Visual Questions
If the concept can be found in the image, the answer to the question is "yes", and otherwise "no".
