2 papers
cs.LG2024
Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data
Samuel C. Hoffman, Payel Das, Karthikeyan Shanmugam +2
Training generative models that capture rich semantics of the data and interpreting the latent representations encoded by such models are very important problems in un-/self-superv…
cs.CL2024
Causal ATE Mitigates Unintended Bias in Controlled Text Generation
Rahul Madhavan, Kahini Wadhawan
We study attribute control in language models through the method of Causal Average Treatment Effect (Causal ATE). Existing methods for the attribute control task in Language Models…