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Found 10 papers, 6 papers with code
IPO: Your Language Model is Secretly a Preference Classifier
Reinforcement learning from human feedback (RLHF) has emerged as the primary method for aligning large language models (LLMs) with human preferences.
Adaptive Urban Planning: A Hybrid Framework for Balanced City Development
Urban planning faces a critical challenge in balancing city-wide infrastructure needs with localized demographic preferences, particularly in rapidly developing regions.
LoRA-Mini : Adaptation Matrices Decomposition and Selective Training
The rapid advancements in large language models (LLMs) have revolutionized natural language processing, creating an increased need for efficient, task-specific fine-tuning methods.
Are VLMs Really Blind
Vision Language Models excel in handling a wide range of complex tasks, including Optical Character Recognition (OCR), Visual Question Answering (VQA), and advanced geometric reasoning.
Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning
We incorporated this pipeline for tasks involving geometry, algebra, and counting.
Give me a hint: Can LLMs take a hint to solve math problems?
While state-of-the-art LLMs have shown poor logical and basic mathematical reasoning, recent works try to improve their problem-solving abilities using prompting techniques.
Attention Shift: Steering AI Away from Unsafe Content
This study investigates the generation of unsafe or harmful content in state-of-the-art generative models, focusing on methods for restricting such generations.
Snowy Scenes,Clear Detections: A Robust Model for Traffic Light Detection in Adverse Weather Conditions
With the rise of autonomous vehicles and advanced driver-assistance systems (ADAS), ensuring reliable object detection in all weather conditions is crucial for safety and efficiency.
Unmasking the Veil: An Investigation into Concept Ablation for Privacy and Copyright Protection in Images
In this paper, we extend the study of concept ablation within pre-trained models as introduced in 'Ablating Concepts in Text-to-Image Diffusion Models' by (Kumari et al., 2022).
Confidence Is All You Need for MI Attacks
In this attack, adversaries aim to determine whether a particular point was used during the training of a target model.
