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Dr. SoW: Density Ratio of Strong-over-weak LLMs for Reducing the Cost of Human Annotation in Preference Tuning
Guangxuan Xu, Kai Xu, Shivchander Sudalairaj +2
Preference tuning relies on high-quality human preference data, which is often expensive and time-consuming to gather. In this paper, we introduce Dr.SoW (Density Ratio of Strong o…
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models
Xiaoxiao He, Quan Dao, Ligong Han +14
Discrete diffusion models have achieved success in tasks like image generation and masked language modeling but face limitations in controlled content editing. We introduce DICE (D…
Urban context and delivery performance: Modelling service time for cargo bikes and vans across diverse urban environments
Maxwell Schrader, Navish Kumar, Esben Sørig +5
Light goods vehicles (LGV) used extensively in the last mile of delivery are one of the leading polluters in cities. Cargo-bike logistics and Light Electric Vehicles (LEVs) have be…
Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models
Xinxi Zhang, Song Wen, Ligong Han +6
Adapting large-scale pre-trained generative models in a parameter-efficient manner is gaining traction. Traditional methods like low rank adaptation achieve parameter efficiency by…
Differentially Private Synthetic Data Generation for Relational Databases
Kaveh Alimohammadi, Hao Wang, Ojas Gulati +2
Existing differentially private (DP) synthetic data generation mechanisms typically assume a single-source table. In practice, data is often distributed across multiple tables with…