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Found 7 papers, 4 papers with code
Locating and Editing Factual Associations in GPT
To test our hypothesis that these computations correspond to factual association recall, we modify feed-forward weights to update specific factual associations using Rank-One Model Editing (ROME).
Mass-Editing Memory in a Transformer
Recent work has shown exciting promise in updating large language models with new memories, so as to replace obsolete information or add specialized knowledge.
Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims
We present a study on the efficacy of adversarial training on transformer neural network models, with respect to the task of detecting check-worthy claims.
Linearity of Relation Decoding in Transformer Language Models
Linear relation representations may be obtained by constructing a first-order approximation to the LM from a single prompt, and they exist for a variety of factual, commonsense, and linguistic relations.
Through-Wall Pose Imaging in Real-Time with a Many-to-Many Encoder/Decoder Paradigm
Overcoming the visual barrier and developing "see-through vision" has been one of mankind's long-standing dreams.
A Dashboard for Mitigating the COVID-19 Misinfodemic
This paper describes the current milestones achieved in our ongoing project that aims to understand the surveillance of, impact of and intervention on COVID-19 misinfodemic on Twitter.
Exploiting and Defending Against the Approximate Linearity of Apple's NeuralHash
Perceptual hashes map images with identical semantic content to the same $n$-bit hash value, while mapping semantically-different images to different hashes.
