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Found 18 papers, 10 papers with code
LLM-Virus: Evolutionary Jailbreak Attack on Large Language Models
While safety-aligned large language models (LLMs) are increasingly used as the cornerstone for powerful systems such as multi-agent frameworks to solve complex real-world problems, they still suffer from potential adversarial queries, such as jailbreak attacks, which attempt to induce harmful content.
Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence
Large vision-language models (LVLMs) have made substantial progress in integrating large language models (LLMs) with visual inputs, enabling advanced multimodal reasoning.
Context-DPO: Aligning Language Models for Context-Faithfulness
Reliable responses from large language models (LLMs) require adherence to user instructions and retrieved information.
Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks
Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task.
On the Role of Attention Heads in Large Language Model Safety
In light of this, recent research on safety mechanisms has emerged, revealing that when safety representations or component are suppressed, the safety capability of LLMs are compromised.
G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks
Recent advancements in large language model (LLM)-based agents have demonstrated that collective intelligence can significantly surpass the capabilities of individual agents, primarily due to well-crafted inter-agent communication topologies.
DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector
Graph Anomaly Detection (GAD) is crucial for identifying abnormal entities within networks, garnering significant attention across various fields.
Neuron-Level Sequential Editing for Large Language Models
This work explores sequential model editing in large language models (LLMs), a critical task that involves modifying internal knowledge within LLMs continuously through multi-round editing, each incorporating updates or corrections to adjust the model outputs without the need for costly retraining.
Text-guided Diffusion Model for 3D Molecule Generation
The de novo generation of molecules with targeted properties is crucial in biology, chemistry, and drug discovery.
AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models
To address this, we introduce AlphaEdit, a novel solution that projects perturbation onto the null space of the preserved knowledge before applying it to the parameters.
Mind Scramble: Unveiling Large Language Model Psychology Via Typoglycemia
Research into the external behaviors and internal mechanisms of large language models (LLMs) has shown promise in addressing complex tasks in the physical world.
StruEdit: Structured Outputs Enable the Fast and Accurate Knowledge Editing for Large Language Models
To achieve such ideal question-answering systems, locating and then editing outdated knowledge in the natural language outputs is a general target of popular knowledge editing methods.
The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs
Based on observations, we innovatively introduce the Heterophily Snowflake Hypothesis and provide an effective solution to guide and facilitate research on heterophilic graphs and beyond.
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Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness
We first revisit the current factuality enhancement methods and evaluate their effectiveness in enhancing factual accuracy.
MolTC: Towards Molecular Relational Modeling In Language Models
Molecular Relational Learning (MRL), aiming to understand interactions between molecular pairs, plays a pivotal role in advancing biochemical research.
Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts
In this paper, we address the issue of modeling and estimating changes in the state of the spatio-temporal dynamical systems based on a sequence of observations like video frames.
EXGC: Bridging Efficiency and Explainability in Graph Condensation
Graph representation learning on vast datasets, like web data, has made significant strides.
Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness
Specifically, GST initially constructs a topology & semantic anchor at a low training cost, followed by performing dynamic sparse training to align the sparse graph with the anchor.
