Latest AI and machine learning research in cultural competence for healthcare professionals.
Objective: To optimize in-context learning in biomedical natural language processing by improving example selection. Methods: We introduce a novel multi-mode retrieval-augmented generation (MMRAG) framework, which integrates four retrieval strategies: (1) Random Mode, selecting examples arbitrarily; (2) Top Mode, retrieving the most relevant examples based on similarity; (3) Diversity Mode, ensu...
Access to sexual and reproductive health information remains a challenge in many communities globally, due to cultural taboos and limited availability of healthcare providers. Public health organizations are increasingly turning to Large Language Models (LLMs) to improve access to timely and personalized information. However, recent HCI scholarship indicates that significant challenges remain in...
There has been significant prior work using templates to study bias against demographic attributes in MLMs. However, these have limitations: they ov...
Alignment techniques have become central to ensuring that Large Language Models (LLMs) generate outputs consistent with human values. However, exist...
Heart rate (HR) estimation via remote photoplethysmography (rPPG) offers a non-invasive solution for health monitoring. However, traditional single-...
We explore how private synthetic text can be generated by suitably prompting a large language model (LLM). This addresses a challenge for organizati...
The recent advances in large language models (LLMs) have revolutionized industries such as finance, marketing, and customer service by enabling soph...
The integration of large language models (LLMs) on low-power edge devices such as Raspberry Pi, known as edge language models (ELMs), has introduced...
Language is far more than a communication tool. A wealth of information - including but not limited to the identities, psychological states, and soc...
While deepfake technologies have predominantly been criticized for potential misuse, our study demonstrates their significant potential as tools for...
Rapid advancements in Large Language Models (LLMs) have accelerated their integration into automated visualization code generation applications. Des...
Bias issues of neural networks garner significant attention along with its promising advancement. Among various bias issues, mitigating two predomin...
The co-design of robot morphology and neural control typically requires using reinforcement learning to approximate a unique control policy gradient...
The integration of vision-language models into robotic systems constitutes a significant advancement in enabling machines to interact with their sur...
Large language models (LLMs) have been shown to propagate and even amplify gender bias, in English and other languages, in specific or constrained c...
We empirically investigate the camera bias of person re-identification (ReID) models. Previously, camera-aware methods have been proposed to address...
Image Captioning for state-of-the-art VLMs has significantly improved over time; however, this comes at the cost of increased computational complexi...
Multimodal visual language models are gaining prominence in open-world applications, driven by advancements in model architectures, training techniq...
Deep learning model effectiveness in classification tasks is often challenged by the quality and quantity of training data whenever they are affecte...
Stereotype biases in Large Multimodal Models (LMMs) perpetuate harmful societal prejudices, undermining the fairness and equity of AI applications. ...