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Found 8 papers, 4 papers with code
Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs
To address these challenges, we introduce a self-instructed in-context learning framework that empowers LLMs to deliver more effective responses by generating reliable derived prompts to construct informative contextual environments.
Detecting AI Flaws: Target-Driven Attacks on Internal Faults in Language Models
While the primary focus of the target models is on open-source LLMs, the fine-tuned ToxDet can also be transferred to attack black-box models such as GPT-4o, achieving notable results.
ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models
Colonoscopy analysis, particularly automatic polyp segmentation and detection, is essential for assisting clinical diagnosis and treatment.
CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation
To the best of our knowledge, CBLab is the first infrastructure supporting traffic control policy optimization in large-scale urban scenarios.
Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training
Besides, we conduct further analysis to better verify the effectiveness of different components of our approach and various settings of pre-training.
Comprehensive Visual Question Answering on Point Clouds through Compositional Scene Manipulation
To tackle this problem, we propose the CLEVR3D, a large-scale VQA-3D dataset consisting of 171K questions from 8, 771 3D scenes.
Incentives in Two-sided Matching Markets with Prediction-enhanced Preference-formation
Here, we first introduce this type of strategic behavior, which we call an `adversarial interaction attack'.
MDR Cluster-Debias: A Nonlinear WordEmbedding Debiasing Pipeline
This indicates that word embeddings encode gender bias in still other ways, not necessarily captured by upstream tests.
