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Found 11 papers, 10 papers with code
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing
Although large language models (LLMs) have achieved significant success, their vulnerability to adversarial perturbations, including recent jailbreak attacks, has raised considerable concerns.
A Survey on Data Selection for Language Models
A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training.
Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing
Aligned large language models (LLMs) are vulnerable to jailbreaking attacks, which bypass the safeguards of targeted LLMs and fool them into generating objectionable content.
Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling
Uncertainty decomposition refers to the task of decomposing the total uncertainty of a model into data (aleatoric) uncertainty, resulting from the inherent complexity or ambiguity of the data, and model (epistemic) uncertainty, resulting from the lack of knowledge in the model.
Certified Robustness for Large Language Models with Self-Denoising
This largely falls into the study of certified robust LLMs, i. e., all predictions of LLM are certified to be correct in a local region around the input.
Improving Diffusion Models for Scene Text Editing with Dual Encoders
Scene text editing is a challenging task that involves modifying or inserting specified texts in an image while maintaining its natural and realistic appearance.
PromptBoosting: Black-Box Text Classification with Ten Forward Passes
Instead of directly optimizing in prompt space, PromptBoosting obtains a small pool of prompts via a gradient-free approach and then constructs a large pool of weak learners by pairing these prompts with different elements of the LM's output distribution.
TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization
Robustness evaluation against adversarial examples has become increasingly important to unveil the trustworthiness of the prevailing deep models in natural language processing (NLP).
Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet
In this paper, we propose a new unsupervised method for HowNet-based Chinese WSD, which exploits the masked language model task of pre-trained language models.
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations
Adversarial attacking aims to fool deep neural networks with adversarial examples.
OpenAttack: An Open-source Textual Adversarial Attack Toolkit
Textual adversarial attacking has received wide and increasing attention in recent years.
