Continual learning is a rapidly evolving area of research that focuses on developing models capable of learning from sequentially arriving data streams, similar to human learning. It addresses the challenges of adapting to new information while retaining previously acquired knowledge. This field is particularly relevant in scenarios where models must perform well on multiple tasks… →
One of the central challenges in spatiotemporal prediction is efficiently handling the vast and complex datasets produced in diverse domains such as environmental monitoring, epidemiology, and cloud computing. Spatiotemporal datasets consist of time-evolving data observed at different spatial locations, making their analysis critical for tasks like forecasting air quality, tracking disease spread, or predicting resource… →
Recent advances in multimodal foundation models like GPT-4V have shown strong performance in general visual and textual data tasks. However, adapting these models to specialized domains like biomedicine requires large, domain-specific instruction datasets. While automatic dataset generation has been explored, these datasets often need more alignment with expert knowledge, limiting their real-world applicability. Instruction tuning,… →
Natural language processing (NLP) has experienced a surge in progress with the emergence of large language models (LLMs), which are utilized in various applications such as text generation, translation, and conversational agents. These models can process and understand human languages at an unprecedented level, enabling seamless communication between machines and users. However, despite their success,… →
Large language models (LLMs) and image generators face a critical challenge known as model collapse. This phenomenon occurs when the performance of these AI systems deteriorates due to the increasing presence of AI-generated data in their training datasets. As generative AI evolves, evidence suggests that retraining models on their outputs can lead to various anomalies… →
Table of contents Introduction to Chunking in RAG Overview of Chunking in RAG Detailed Analysis of Each Chunking Method Choosing the Right Chunking Technique Conclusion Introduction to Chunking in RAG In natural language processing (NLP), Retrieval-Augmented Generation (RAG) is emerging as a powerful tool for information retrieval and contextual text generation. RAG combines the strengths… →
Large Language Models (LLMs) evaluate and interpret links between words or tokens in a sequence primarily through the self-attention mechanism. However, this module’s time and memory complexity rises quadratically with sequence length, which is a disadvantage. Longer sequences demand exponentially more memory and processing, which makes scaling LLMs for applications involving longer contexts inefficient and… →
Human-sensing applications such as activity recognition, fall detection, and health monitoring have been revolutionized by advancements in artificial intelligence (AI) and machine learning technologies. These applications can significantly impact health management by monitoring human behavior and providing critical data for health assessments. However, due to the variability in individual behaviors, environmental factors, and the physical… →
Protein language models (pLMs), trained on protein sequence databases, aim to capture the fitness landscape for property prediction and design tasks. While scaling these models has become common, it assumes that the source databases accurately reflect the fitness landscape, which may not be true. Understanding protein function was historically tied to predicting structure based on… →