Surveys

Fine-Grained Bias Detection in LLM: Enhancing detection mechanisms for nuanced biases

Recent advancements in Artificial Intelligence, particularly in Large Language Models (LLMs), have transformed natural language processing by improving generative capabilities. However, detecting biases embedded within these models remains a challenge. Subtle biases can propagate misinformation, influence decision-making, and reinforce stereotypes, raising ethical concerns. This study presents a...

AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data

Recent advances in generative models have sparked research on improving model fairness with AI-generated data. However, existing methods often face limitations in the diversity and quality of synthetic data, leading to compromised fairness and overall model accuracy. Moreover, many approaches rely on the availability of demographic group labels, which are often costly to annotate. This paper pro...

PoSSUM: A Protocol for Surveying Social-media Users with Multimodal LLMs

This paper introduces PoSSUM, an open-source protocol for unobtrusive polling of social-media users via multimodal Large Language Models (LLMs). PoS...

Removing Geometric Bias in One-Class Anomaly Detection with Adaptive Feature Perturbation

One-class anomaly detection aims to detect objects that do not belong to a predefined normal class. In practice training data lack those anomalous s...

Visual Cues of Gender and Race are Associated with Stereotyping in Vision-Language Models

Current research on bias in Vision Language Models (VLMs) has important limitations: it is focused exclusively on trait associations while ignoring ...

SHAPE : Self-Improved Visual Preference Alignment by Iteratively Generating Holistic Winner

Large Visual Language Models (LVLMs) increasingly rely on preference alignment to ensure reliability, which steers the model behavior via preference...

Simulating the Real World: A Unified Survey of Multimodal Generative Models

Understanding and replicating the real world is a critical challenge in Artificial General Intelligence (AGI) research. To achieve this, many existi...

Towards Understanding Text Hallucination of Diffusion Models via Local Generation Bias

Score-based diffusion models have achieved incredible performance in generating realistic images, audio, and video data. While these models produce ...

Biased Heritage: How Datasets Shape Models in Facial Expression Recognition

In recent years, the rapid development of artificial intelligence (AI) systems has raised concerns about our ability to ensure their fairness, that ...

An Analytical Theory of Power Law Spectral Bias in the Learning Dynamics of Diffusion Models

We developed an analytical framework for understanding how the learned distribution evolves during diffusion model training. Leveraging the Gaussian...

Unveiling the Potential of Segment Anything Model 2 for RGB-Thermal Semantic Segmentation with Language Guidance

The perception capability of robotic systems relies on the richness of the dataset. Although Segment Anything Model 2 (SAM2), trained on large datas...

Disentangled Knowledge Tracing for Alleviating Cognitive Bias

In the realm of Intelligent Tutoring System (ITS), the accurate assessment of students' knowledge states through Knowledge Tracing (KT) is crucial f...

EchoQA: A Large Collection of Instruction Tuning Data for Echocardiogram Reports

We introduce a novel question-answering (QA) dataset using echocardiogram reports sourced from the Medical Information Mart for Intensive Care datab...

MindSimulator: Exploring Brain Concept Localization via Synthetic FMRI

Concept-selective regions within the human cerebral cortex exhibit significant activation in response to specific visual stimuli associated with par...

On the Relationship Between Double Descent of CNNs and Shape/Texture Bias Under Learning Process

The double descent phenomenon, which deviates from the traditional bias-variance trade-off theory, attracts considerable research attention; however...

Aerial Infrared Health Monitoring of Solar Photovoltaic Farms at Scale

Solar photovoltaic (PV) farms represent a major source of global renewable energy generation, yet their true operational efficiency often remains un...

Biomedical Foundation Model: A Survey

Foundation models, first introduced in 2021, are large-scale pre-trained models (e.g., large language models (LLMs) and vision-language models (VLMs...

The Reliability of LLMs for Medical Diagnosis: An Examination of Consistency, Manipulation, and Contextual Awareness

Universal healthcare access is critically needed, especially in resource-limited settings. Large Language Models (LLMs) offer promise for democratiz...

A Survey on Ordinal Regression: Applications, Advances and Prospects

Ordinal regression refers to classifying object instances into ordinal categories. Ordinal regression is crucial for applications in various areas l...

Detecting Heel Strike and toe off Events Using Kinematic Methods and LSTM Models

Accurate gait event detection is crucial for gait analysis, rehabilitation, and assistive technology, particularly in exoskeleton control, where pre...