Summary of Par: Prompt-aware Token Reduction Method For Efficient Large Multimodal Models, by Yingen Liu et al.
PAR: Prompt-Aware Token Reduction Method for Efficient Large Multimodal Modelsby Yingen Liu, Fan Wu, Ruihui…
PAR: Prompt-Aware Token Reduction Method for Efficient Large Multimodal Modelsby Yingen Liu, Fan Wu, Ruihui…
Enhancing Performance of Point Cloud Completion Networks with Consistency Lossby Kevin Tirta Wijaya, Christofel Rio…
Examining the Prevalence and Dynamics of AI-Generated Media in Art Subredditsby Hana Matatov, Marianne Aubin…
DA-Code: Agent Data Science Code Generation Benchmark for Large Language Modelsby Yiming Huang, Jianwen Luo,…
Improving the portability of predicting students performance models by using ontologiesby Javier Lopez Zambrano, Juan…
Positive-Augmented Contrastive Learning for Vision-and-Language Evaluation and Trainingby Sara Sarto, Nicholas Moratelli, Marcella Cornia, Lorenzo…
SparseGrad: A Selective Method for Efficient Fine-tuning of MLP Layersby Viktoriia Chekalina, Anna Rudenko, Gleb…
The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarksby Isaac R. Galatzer-Levy,…
Exploring Efficient Foundational Multi-modal Models for Video Summarizationby Karan Samel, Apoorva Beedu, Nitish Sontakke, Irfan…
Using LLMs to Discover Legal Factorsby Morgan Gray, Jaromir Savelka, Wesley Oliver, Kevin AshleyFirst submitted…