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Found 5 papers, 2 papers with code
Large Language Models Overcome the Machine Penalty When Acting Fairly but Not When Acting Selfishly or Altruistically
We design three types of LLMs: (i) Cooperative, aiming to assist its human associate; (ii) Selfish, focusing solely on maximizing its self-interest; and (iii) Fair, balancing its own and collective interest, while slightly prioritizing self-interest.
Emergence of Social Norms in Generative Agent Societies: Principles and Architecture
Our experiments deployed in the Smallville sandbox game environment demonstrate the capability of our architecture to establish social norms and reduce social conflicts within generative MASs.
GenAD: Generative End-to-End Autonomous Driving
We then employ a variational autoencoder to learn the future trajectory distribution in a structural latent space for trajectory prior modeling.
DAIRstega: Dynamically Allocated Interval-Based Generative Linguistic Steganography with Roulette Wheel
Based on the invisible characteristics of LS, we give three constraints that need to be met to design the function.
UP4LS: User Profile Constructed by Multiple Attributes for Enhancing Linguistic Steganalysis
The extracted features are mapped to high-dimensional user features via the deep-learning model of the method to be improved.
