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Found 13 papers, 0 papers with code
Multi-hop Question Answering under Temporal Knowledge Editing
Multi-hop question answering (MQA) under knowledge editing (KE) has garnered significant attention in the era of large language models.
PROMPT-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression
Large language models (LLMs) have shown exceptional abilities for multiple different natural language processing tasks.
Dialectical Alignment: Resolving the Tension of 3H and Security Threats of LLMs
With the rise of large language models (LLMs), ensuring they embody the principles of being helpful, honest, and harmless (3H), known as Human Alignment, becomes crucial.
MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning
Adapting large language models (LLMs) to new domains/tasks and enabling them to be efficient lifelong learners is a pivotal challenge.
MONAL: Model Autophagy Analysis for Modeling Human-AI Interactions
The increasing significance of large models and their multi-modal variants in societal information processing has ignited debates on social safety and ethics.
Improving Faithfulness for Vision Transformers
However, ViTs suffer from issues with explanation faithfulness, as their focal points are fragile to adversarial attacks and can be easily changed with even slight perturbations on the input image.
Fair Text-to-Image Diffusion via Fair Mapping
In this paper, we address the limitations of existing text-to-image diffusion models in generating demographically fair results when given human-related descriptions.
Differentially Private Natural Language Models: Recent Advances and Future Directions
In this paper, we provide the first systematic review of recent advances in DP deep learning models in NLP.
SEAT: Stable and Explainable Attention
Results show that SEAT is more stable against different perturbations and randomness while also keeps the explainability of attention, which indicates it is a more faithful explanation.
Faster Rates of Private Stochastic Convex Optimization
Specifically, we first show that under some mild assumptions on the loss functions, there is an algorithm whose output could achieve an upper bound of $\tilde{O}((\frac{1}{\sqrt{n}}+\frac{\sqrt{d\log \frac{1}{\delta}}}{n\epsilon})^\frac{\theta}{\theta-1})$ for $(\epsilon, \delta)$-DP when $\theta\geq 2$, here $n$ is the sample size and $d$ is the dimension of the space.
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data
To better understand the challenges arising from irregular data distribution, in this paper we provide the first study on the problem of DP-SCO with heavy-tailed data in the high dimensional space.
Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees
To address this issue, we propose in this paper the first DP version of (Gradient) EM algorithm with statistical guarantees.
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data
In the second part of the paper, we extend our idea to the problem of estimating non-linear regressions and show similar results as in GLMs for both multivariate Gaussian and sub-Gaussian cases.
