Genre
SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning Y exiao He
The pre-trained Large Language Models (LLMs) can be adapted for many downstream tasks and tailored to align with human preferences through fine-tuning.
Back-Modality: Leveraging Modal Transformation for Data Augmentation
On one hand, it operates as a general data augmentation strategy.
PersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Models
Models (LLMs) can sometimes surpass those annotated by experts, such as journalists, according to human evaluations. However, there is limited research on whether these generic summaries meet the individual needs of ordinary people.
MVInpainter: Learning Multi-View Consistent Inpainting to Bridge 2D and 3D Editing
Novel View Synthesis (NVS) and 3D generation have recently achieved prominent improvements.
e197fe307eb3467035f892dc100d570a-Supplemental-Conference.pdf
The process for calculating these metrics is described in Appendix C. Moreover, to ensure the comparability between prediction performance metrics and driving performance metrics in the radar plot, we normalize all metrics to the scale of [0, 1]. In the subsequent section, we provide an overview of the DESPOT planner. These two values can only be inferred from history. The safety is represented by the normalized collision rate.
