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Found 23 papers, 13 papers with code
Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding
Large language models (LLMs) have shown remarkable capabilities in natural language processing; however, they still face difficulties when tasked with understanding lengthy contexts and executing effective question answering.
CLIBE: Detecting Dynamic Backdoors in Transformer-based NLP Models
CLIBE injects a "few-shot perturbation" into the suspect Transformer model by crafting optimized weight perturbation in the attention layers to make the perturbed model classify a limited number of reference samples as a target label.
LABOR-LLM: Language-Based Occupational Representations with Large Language Models
For the task of next job prediction, we demonstrate that models trained with our approach outperform several alternatives in terms of predictive performance on the survey data, including traditional econometric models, CAREER, and LLMs with in-context learning, even though the LLM can in principle predict job titles that are not allowed in the survey data.
DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models
In this paper, we introduce DP-MemArc, a novel training framework aimed at reducing the memory costs of large language models while emphasizing the protection of user data privacy.
Tool-Planner: Task Planning with Clusters across Multiple Tools
Experiments show that our approach demonstrates a high pass and win rate across different datasets and optimizes the planning scheme for tool learning in models such as GPT-4 and Claude 3, showcasing the potential of our method.
Could It Be Generated? Towards Practical Analysis of Memorization in Text-To-Image Diffusion Models
Based on the correlation, we propose to utilize inversion techniques to verify the safety of target images against memorization and measure the extent to which they are memorized.
TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment
Specifically, we identify four critical protection properties that existing methods fail to simultaneously satisfy: (1) maintaining protection after a model is physically copied; (2) authorizing model access at request level; (3) safeguarding runtime reverse engineering; (4) achieving high security with negligible runtime overhead.
ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis
Large language models (LLMs) have achieved commendable accomplishments in various natural language processing tasks.
RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback
Large language models (LLMs) demonstrate exceptional performance in numerous tasks but still heavily rely on knowledge stored in their parameters.
VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
In this paper, we aim to investigate a new yet practical task to craft image and text perturbations using pre-trained VL models to attack black-box fine-tuned models on different downstream tasks.
ReMasker: Imputing Tabular Data with Masked Autoencoding
We present ReMasker, a new method of imputing missing values in tabular data by extending the masked autoencoding framework.
G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering
Through empirical evaluation, comparing G$^2$uardFL with cutting-edge defenses, such as FLAME (USENIX Security 2022) [28] and DeepSight (NDSS 2022) [36], against various backdoor attacks including 3DFed (SP 2023) [20], our results demonstrate its significant effectiveness in mitigating backdoor attacks while having a negligible impact on the aggregated model's performance on benign samples (i. e., the primary task performance).
On the Security Risks of Knowledge Graph Reasoning
Knowledge graph reasoning (KGR) -- answering complex logical queries over large knowledge graphs -- represents an important artificial intelligence task, entailing a range of applications (e. g., cyber threat hunting).
RNN-Guard: Certified Robustness Against Multi-frame Attacks for Recurrent Neural Networks
In this paper, we propose the first certified defense against multi-frame attacks for RNNs called RNN-Guard.
Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python
$\texttt{torch-choice}$ provides a $\texttt{ChoiceDataset}$ data structure to manage databases flexibly and memory-efficiently.
An Embarrassingly Simple Backdoor Attack on Self-supervised Learning
As a new paradigm in machine learning, self-supervised learning (SSL) is capable of learning high-quality representations of complex data without relying on labels.
Reasoning over Multi-view Knowledge Graphs
(ii) It supports complex logical queries with varying relation and view constraints (e. g., with complex topology and/or from multiple views); (iii) It scales up to KGs of large sizes (e. g., millions of facts) and fine-granular views (e. g., dozens of views); (iv) It generalizes to query structures and KG views that are unobserved during training.
ROLAND: Graph Learning Framework for Dynamic Graphs
Finally, we propose a scalable and efficient training approach for dynamic GNNs via incremental training and meta-learning.
CAREER: A Foundation Model for Labor Sequence Data
We fit CAREER to a dataset of 24 million job sequences from resumes, and adjust it on small longitudinal survey datasets.
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification
To overcome the challenges, we propose NeuronFair, a new DNN fairness testing framework that differs from previous work in several key aspects: (1) interpretable - it quantitatively interprets DNNs' fairness violations for the biased decision; (2) effective - it uses the interpretation results to guide the generation of more diverse instances in less time; (3) generic - it can handle both structured and unstructured data.
Enhancing Model Robustness By Incorporating Adversarial Knowledge Into Semantic Representation
Extensive experiments on two real-world tasks show that AdvGraph exhibits better performance compared with previous work: (i) effective - it significantly strengthens the model robustness even under the adaptive attacks setting without negative impact on model performance over legitimate input; (ii) generic - its key component, i. e., the representation of connotative adversarial knowledge is task-agnostic, which can be reused in any Chinese-based NLP models without retraining; and (iii) efficient - it is a light-weight defense with sub-linear computational complexity, which can guarantee the efficiency required in practical scenarios.
SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems
Despite their immense popularity, deep learning-based acoustic systems are inherently vulnerable to adversarial attacks, wherein maliciously crafted audios trigger target systems to misbehave.
Cryptography and Security
TextBugger: Generating Adversarial Text Against Real-world Applications
Deep Learning-based Text Understanding (DLTU) is the backbone technique behind various applications, including question answering, machine translation, and text classification.
