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Found 9 papers, 5 papers with code
Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance
The advancement of Large Vision-Language Models (LVLMs) has increasingly highlighted the critical issue of their tendency to hallucinate non-existing objects in the images.
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
In this paper, we delve into the prospect of growing a strong LLM out of a weak one without the need for acquiring additional human-annotated data.
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression
Additionally, when our analysis is specialized to linear regression in the strongly convex setting, it yields a tighter bound for bias error than the best-known result.
Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
While it is widely acknowledged that the quality of a prompt, such as a question, significantly impacts the quality of the response provided by LLMs, a systematic method for crafting questions that LLMs can better comprehend is still underdeveloped.
Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP
Multi-modal learning has become increasingly popular due to its ability to leverage information from different data sources (e. g., text and images) to improve the model performance.
Towards Understanding Mixture of Experts in Deep Learning
To our knowledge, this is the first result towards formally understanding the mechanism of the MoE layer for deep learning.
Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text Attacks
Adversarial training is the most empirically successful approach in improving the robustness of deep neural networks for image classification. For text classification, however, existing synonym substitution based adversarial attacks are effective but not efficient to be incorporated into practical text adversarial training.
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency
Experiments on three popular datasets using convolutional as well as LSTM models show that PWWS reduces the classification accuracy to the most extent, and keeps a very low word substitution rate.
