The large language models (LLMs) research domain emphasizes aligning these models with human preferences to produce helpful, unbiased, and safe responses. Researchers have made significant strides in training LLMs to improve their ability to understand, comprehend, and interact with human-generated text, enhancing communication between humans and machines. A primary challenge in NLP is teaching LLMs… →

The robotics research field has significantly transformed by integrating large language models (LLMs). These advancements have presented an opportunity to guide robotic systems in solving complex tasks that involve intricate planning and long-horizon manipulation. While robots have traditionally relied on predefined skills and specialized engineering, recent developments show potential in using LLMs to help guide… →

The natural language processing (NLP) field is continuously evolving, with large language models (LLMs) becoming integral to many applications. The push towards fine-tuning these models has become crucial to enhance their specific capabilities without requiring extensive computational resources. Researchers have recently explored ways to modify LLMs to ensure they perform optimally, even with limited computational… →

Graph Neural Network (GNN)–based motion planning has emerged as a promising approach in robotic systems for its efficiency in pathfinding and navigation tasks. This approach leverages GNNs to learn the underlying graph structure of an environment, enabling it to make quick and informed decisions about which paths to take. Let’s delve into the detailed specifics… →

Generic transport equations, comprising time-dependent partial differential equations (PDEs), delineate the evolution of extensive properties in physical systems, encompassing mass, momentum, and energy. Derived from conservation laws, they underpin comprehension of diverse physical phenomena, from mass diffusion to Navier–Stokes equations. Widely applicable across science and engineering, these equations support high-fidelity simulations vital for addressing design… →

In deep learning, especially in NLP, image analysis, and biology, there is an increasing focus on developing models that offer both computational efficiency and robust expressiveness. Attention mechanisms have been revolutionary, allowing for better handling of sequence modeling tasks. However, the computational complexity associated with these mechanisms scales quadratically with sequence length, which becomes a… →

In recent years, the demand for AI and Machine Learning has surged, making ML expertise increasingly vital for job seekers. Additionally, Python has emerged as the primary language for various ML tasks. This article outlines the top ML courses in Python, offering readers the opportunity to enhance their skill set, transition careers, and meet the… →

The surge in powerful Transformer-based language models (LMs) and their widespread use highlights the need for research into their inner workings. Understanding these mechanisms in advanced AI systems is crucial for ensuring their safety, and fairness, and minimizing biases and errors, especially in critical contexts. Consequently, there’s been a notable uptick in research within the… →

Multitask learning (MLT) involves training a single model to perform multiple tasks simultaneously, leveraging shared information to enhance performance. While beneficial, MLT poses challenges in managing large models and optimizing across tasks. Optimizing the average loss may lead to suboptimal performance if tasks progress unevenly. Balancing task performance and optimization strategies is critical for effective MLT.… →

Language models built on Large Language models (LLMs) have been developed for multiple applications, followed by new advancements in enhancing LLMs. However, LLMs lack adaption and personalization to a particular user and task. Users often provide feedback to LLM-based agents through user edits and editing their responses before the final use. In contrast, standard fine-tuning… →