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Found 6 papers, 2 papers with code
Enhancing Efficiency in Sparse Models with Sparser Selection
To address this issue, we present \tool, a novel MoE designed to enhance both the efficacy and efficiency of sparse MoE models.
VRPTEST: Evaluating Visual Referring Prompting in Large Multimodal Models
With recent advancements in Large Multimodal Models (LMMs) across various domains, a novel prompting method called visual referring prompting has emerged, showing significant potential in enhancing human-computer interaction within multimodal systems.
Split and Merge: Aligning Position Biases in Large Language Model based Evaluators
Specifically, PORTIA splits the answers into multiple segments, aligns similar content across candidate answers, and then merges them back into a single prompt for evaluation by LLMs.
No More Fine-Tuning? An Experimental Evaluation of Prompt Tuning in Code Intelligence
Besides, the performance of fine-tuning strongly relies on the amount of downstream data, while in practice, the scenarios with scarce data are common.
Label-Aware Distribution Calibration for Long-tailed Classification
Real-world data usually present long-tailed distributions.
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
We demonstrate the utility of our method for attribute manipulation in autoencoders trained across varied domains, using both human evaluation and automated methods.
Ranked #7 on
Image Generation
on CelebA 256x256
(FID metric)
