arXiv:2411.03814v1 »Full PDF »Large Language Models (LLMs) demonstrate outstanding performance in their
reservoir of knowledge and understanding capabilities, but they have also been
shown to be prone to illegal or unethical reactions when subjected to jailbreak
attacks. To ensure their responsible deployment in critical applications, it is
crucial to understand the safety capabilities and vulnerabilities of LLMs.
Previous works mainly focus on jailbreak in single-round dialogue, overlooking
the potential jailbreak risks in multi-round dialogues, which are a vital way
humans interact with and extract information from LLMs. Some studies have
increasingly concentrated on the risks associated with jailbreak in multi-round
dialogues. These efforts typically involve the use of manually crafted
templates or prompt engineering techniques. However, due to the inherent
complexity of multi-round dialogues, their jailbreak performance is limited. To
solve this problem, we propose a novel multi-round dialogue jailbreaking agent,
emphasizing the importance of stealthiness in identifying and mitigating
potential threats to human values posed by LLMs. We propose a risk
decomposition strategy that distributes risks across multiple rounds of queries
and utilizes psychological strategies to enhance attack strength. Extensive
experiments show that our proposed method surpasses other attack methods and
achieves state-of-the-art attack success rate. We will make the corresponding
code and dataset available for future research. The code will be released soon.Abstract
CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision
Language Models
Artificial intelligence has significantly impacted medical applications,
particularly with the advent of Medical Large Vision Language Models
(Med-LVLMs), sparking optimism for the future of automated and personalized
healthcare. However, the trustworthiness of Med-LVLMs remains unverified,
posing significant risks for future model deployment. In this paper, we
introduce CARES and aim to comprehensively evaluate the Trustworthiness of
Med-LVLMs across the medical domain. We assess the trustworthiness of Med-LVLMs
across five dimensions, including trustfulness, fairness, safety, privacy, and
robustness. CARES comprises about 41K question-answer pairs in both closed and
open-ended formats, covering 16 medical image modalities and 27 anatomical
regions. Our analysis reveals that the models consistently exhibit concerns
regarding trustworthiness, often displaying factual inaccuracies and failing to
maintain fairness across different demographic groups. Furthermore, they are
vulnerable to attacks and demonstrate a lack of privacy awareness. We publicly
release our benchmark and code in https://cares-ai.github.io/.Abstract
GLBench: A Comprehensive Benchmark for Graph with Large Language Models
arXiv:2407.07457v4 »Full PDF »The emergence of large language models (LLMs) has revolutionized the way we
interact with graphs, leading to a new paradigm called GraphLLM. Despite the
rapid development of GraphLLM methods in recent years, the progress and
understanding of this field remain unclear due to the lack of a benchmark with
consistent experimental protocols. To bridge this gap, we introduce GLBench,
the first comprehensive benchmark for evaluating GraphLLM methods in both
supervised and zero-shot scenarios. GLBench provides a fair and thorough
evaluation of different categories of GraphLLM methods, along with traditional
baselines such as graph neural networks. Through extensive experiments on a
collection of real-world datasets with consistent data processing and splitting
strategies, we have uncovered several key findings. Firstly, GraphLLM methods
outperform traditional baselines in supervised settings, with LLM-as-enhancers
showing the most robust performance. However, using LLMs as predictors is less
effective and often leads to uncontrollable output issues. We also notice that
no clear scaling laws exist for current GraphLLM methods. In addition, both
structures and semantics are crucial for effective zero-shot transfer, and our
proposed simple baseline can even outperform several models tailored for
zero-shot scenarios. The data and code of the benchmark can be found at
https://github.com/NineAbyss/GLBench.Abstract
PolyGNN: Polyhedron-based Graph Neural Network for 3D Building
Reconstruction from Point Clouds
Accepted for publication in ISPRS Journal of Photogrammetry and
Remote Sensing
We present PolyGNN, a polyhedron-based graph neural network for 3D building
reconstruction from point clouds. PolyGNN learns to assemble primitives
obtained by polyhedral decomposition via graph node classification, achieving a
watertight and compact reconstruction. To effectively represent
arbitrary-shaped polyhedra in the neural network, we propose a skeleton-based
sampling strategy to generate polyhedron-wise queries. These queries are then
incorporated with inter-polyhedron adjacency to enhance the classification.
PolyGNN is end-to-end optimizable and is designed to accommodate variable-size
input points, polyhedra, and queries with an index-driven batching technique.
To address the abstraction gap between existing city-building models and the
underlying instances, and provide a fair evaluation of the proposed method, we
develop our method on a large-scale synthetic dataset with well-defined ground
truths of polyhedral labels. We further conduct a transferability analysis
across cities and on real-world point clouds. Both qualitative and quantitative
results demonstrate the effectiveness of our method, particularly its
efficiency for large-scale reconstructions. The source code and data are
available at https://github.com/chenzhaiyu/polygnn.Abstract
Artificial intelligence to advance Earth observation: : A review of
models, recent trends, and pathways forward
arXiv:2305.08413v2 »Full PDF »Earth observation (EO) is a prime instrument for monitoring land and ocean
processes, studying the dynamics at work, and taking the pulse of our planet.
This article gives a bird's eye view of the essential scientific tools and
approaches informing and supporting the transition from raw EO data to usable
EO-based information. The promises, as well as the current challenges of these
developments, are highlighted under dedicated sections. Specifically, we cover
the impact of (i) Computer vision; (ii) Machine learning; (iii) Advanced
processing and computing; (iv) Knowledge-based AI; (v) Explainable AI and
causal inference; (vi) Physics-aware models; (vii) User-centric approaches; and
(viii) the much-needed discussion of ethical and societal issues related to the
massive use of ML technologies in EO.Abstract
Event Stream based Sign Language Translation: A High-Definition
Benchmark Dataset and A New Algorithm
First Large-scale and High-Definition Benchmark Dataset for
Event-based Sign Language Translation
Sign Language Translation (SLT) is a core task in the field of AI-assisted
disability. Unlike traditional SLT based on visible light videos, which is
easily affected by factors such as lighting, rapid hand movements, and privacy
breaches, this paper proposes the use of high-definition Event streams for SLT,
effectively mitigating the aforementioned issues. This is primarily because
Event streams have a high dynamic range and dense temporal signals, which can
withstand low illumination and motion blur well. Additionally, due to their
sparsity in space, they effectively protect the privacy of the target person.
More specifically, we propose a new high-resolution Event stream sign language
dataset, termed Event-CSL, which effectively fills the data gap in this area of
research. It contains 14,827 videos, 14,821 glosses, and 2,544 Chinese words in
the text vocabulary. These samples are collected in a variety of indoor and
outdoor scenes, encompassing multiple angles, light intensities, and camera
movements. We have benchmarked existing mainstream SLT works to enable fair
comparison for future efforts. Based on this dataset and several other
large-scale datasets, we propose a novel baseline method that fully leverages
the Mamba model's ability to integrate temporal information of CNN features,
resulting in improved sign language translation outcomes. Both the benchmark
dataset and source code will be released on
https://github.com/Event-AHU/OpenESLAbstract
arXiv:2408.07009v1 »Full PDF »We introduce Imagen 3, a latent diffusion model that generates high quality
images from text prompts. We describe our quality and responsibility
evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at
the time of evaluation. In addition, we discuss issues around safety and
representation, as well as methods we used to minimize the potential harm of
our models.Abstract
Benchmarking Trustworthiness of Multimodal Large Language Models: A
Comprehensive Study
Despite the superior capabilities of Multimodal Large Language Models (MLLMs)
across diverse tasks, they still face significant trustworthiness challenges.
Yet, current literature on the assessment of trustworthy MLLMs remains limited,
lacking a holistic evaluation to offer thorough insights into future
improvements. In this work, we establish MultiTrust, the first comprehensive
and unified benchmark on the trustworthiness of MLLMs across five primary
aspects: truthfulness, safety, robustness, fairness, and privacy. Our benchmark
employs a rigorous evaluation strategy that addresses both multimodal risks and
cross-modal impacts, encompassing 32 diverse tasks with self-curated datasets.
Extensive experiments with 21 modern MLLMs reveal some previously unexplored
trustworthiness issues and risks, highlighting the complexities introduced by
the multimodality and underscoring the necessity for advanced methodologies to
enhance their reliability. For instance, typical proprietary models still
struggle with the perception of visually confusing images and are vulnerable to
multimodal jailbreaking and adversarial attacks; MLLMs are more inclined to
disclose privacy in text and reveal ideological and cultural biases even when
paired with irrelevant images in inference, indicating that the multimodality
amplifies the internal risks from base LLMs. Additionally, we release a
scalable toolbox for standardized trustworthiness research, aiming to
facilitate future advancements in this important field. Code and resources are
publicly available at: https://multi-trust.github.io/.Abstract
An Empirical Study of Training State-of-the-Art LiDAR Segmentation
Models
Preprint; 17 pages, 4 figures, 7 tables; Code at
https://github.com/open-mmlab/mmdetection3d
In the rapidly evolving field of autonomous driving, precise segmentation of
LiDAR data is crucial for understanding complex 3D environments. Traditional
approaches often rely on disparate, standalone codebases, hindering unified
advancements and fair benchmarking across models. To address these challenges,
we introduce MMDetection3D-lidarseg, a comprehensive toolbox designed for the
efficient training and evaluation of state-of-the-art LiDAR segmentation
models. We support a wide range of segmentation models and integrate advanced
data augmentation techniques to enhance robustness and generalization.
Additionally, the toolbox provides support for multiple leading sparse
convolution backends, optimizing computational efficiency and performance. By
fostering a unified framework, MMDetection3D-lidarseg streamlines development
and benchmarking, setting new standards for research and application. Our
extensive benchmark experiments on widely-used datasets demonstrate the
effectiveness of the toolbox. The codebase and trained models have been
publicly available, promoting further research and innovation in the field of
LiDAR segmentation for autonomous driving.Abstract
Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous
Federated Learning in Vehicular Edge Computing
This paper has been accepted by China Communications.The source code
has been released at:https://...
In vehicle edge computing (VEC), asynchronous federated learning (AFL) is
used, where the edge receives a local model and updates the global model,
effectively reducing the global aggregation latency.Due to different amounts of
local data,computing capabilities and locations of the vehicles, renewing the
global model with same weight is inappropriate.The above factors will affect
the local calculation time and upload time of the local model, and the vehicle
may also be affected by Byzantine attacks, leading to the deterioration of the
vehicle data. However, based on deep reinforcement learning (DRL), we can
consider these factors comprehensively to eliminate vehicles with poor
performance as much as possible and exclude vehicles that have suffered
Byzantine attacks before AFL. At the same time, when aggregating AFL, we can
focus on those vehicles with better performance to improve the accuracy and
safety of the system. In this paper, we proposed a vehicle selection scheme
based on DRL in VEC. In this scheme, vehicle s mobility, channel conditions
with temporal variations, computational resources with temporal variations,
different data amount, transmission channel status of vehicles as well as
Byzantine attacks were taken into account.Simulation results show that the
proposed scheme effectively improves the safety and accuracy of the global
model.Abstract
16737 latest Fairness/Ethics + ML/AI papers
