Vineet Kumar is a consulting intern at MarktechPost. He is currently pursuing his BS from the Indian Institute of Technology(IIT), Kanpur. He is a Machine Learning enthusiast. He is passionate about research and the latest advancements in Deep Learning, Computer Vision, and related fields.
Tokenization plays a fundamental role in the performance and scalability of Large Language Models (LLMs). Despite being a critical component, its influence on model...
What is an Agent?
An agent is a Large Language Model (LLM)-powered system that can decide its own workflow. Unlike traditional chatbots, which operate on...
Large language models (LLMs) have shown remarkable abilities in language tasks and reasoning, but their capacity for autonomous planning—especially in complex, multi-step scenarios—remains limited....
Evaluating conversational AI systems powered by large language models (LLMs) presents a critical challenge in artificial intelligence. These systems must handle multi-turn dialogues, integrate...
Humans possess an extraordinary ability to localize sound sources and interpret their environment using auditory cues, a phenomenon termed spatial hearing. This capability enables...
Imagine having a personal chatbot that can answer questions directly from your documents—be it PDFs, research papers, or books. With Retrieval-Augmented Generation (RAG), this...
Generative Large Multimodal Models (LMMs), such as LLaVA and Qwen-VL, excel in vision-language (VL) tasks like image captioning and visual question answering (VQA). However,...
The memory footprint of the key-value (KV) cache can be a bottleneck when serving large language models (LLMs), as it scales proportionally with both...
Vision-language models (VLMs), capable of processing both images and text, have gained immense popularity due to their versatility in solving a wide range of...
In machine learning, multi-task learning (MTL) has emerged as a powerful paradigm that enables concurrent training of multiple interrelated algorithms. By exploiting the inherent...
Accurate propagation modeling is paramount for effective radio deployments, coverage analysis, and interference mitigation in wireless communications. Path loss modeling, a widely adopted approach,...
