5 papers
Sample Where You Struggle: Sharpening Base Model Reasoning via Entropy-Guided Power Sampling
Hong Guo, Nianhui Guo, Christoph Meinel +1
Sampling from the sequence-level power distribution elicits RL-level reasoning from base language models without any parameter updates, but the standard Metropolis--Hastings…
Image Tokens Matter: Mitigating Hallucination in Discrete Tokenizer-based Large Vision-Language Models via Latent Editing
Weixing Wang, Zifeng Ding, Jindong Gu +4
Large Vision-Language Models (LVLMs) with discrete image tokenizers unify multimodal representations by encoding visual inputs into a finite set of tokens. Despite their effectiven…
SeCoKD: Aligning Large Language Models for In-Context Learning with Fewer Shots
Weixing Wang, Haojin Yang, Christoph Meinel
Previous studies have shown that demonstrations can significantly help Large Language Models (LLMs ) perform better on the given tasks. However, this so-called In-Context Learning…
Generalized Categories Discovery for Long-tailed Recognition
Ziyun Li, Christoph Meinel, Haojin Yang
Generalized Class Discovery (GCD) plays a pivotal role in discerning both known and unknown categories from unlabeled datasets by harnessing the insights derived from a labeled set…
Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection
Weixing Wang, Haojin Yang, Christoph Meinel +3
In recent years, there has been a growing interest in using Machine Learning (ML), especially Deep Learning (DL) to solve Network Intrusion Detection (NID) problems. However, the f…