3 citations · 6 across the 7 of their papers we have counts for
7 papers
Predictive Batch Scheduling: Accelerating Language Model Training Through Loss-Aware Sample Prioritization
Sumedh Rasal
We introduce Predictive Batch Scheduling (PBS), a novel training optimization technique that accelerates language model convergence by dynamically prioritizing high-loss samples du…
A Multi-LLM Orchestration Engine for Personalized, Context-Rich Assistance
Sumedh Rasal
In recent years, large language models have demonstrated remarkable capabilities in natural language understanding and generation. However, these models often struggle with halluci…
Optimal Decision Making Through Scenario Simulations Using Large Language Models
Sumedh Rasal, E. J. Hauer
The rapid evolution of Large Language Models (LLMs) has markedly expanded their application across diverse domains, transforming how complex problems are approached and solved. Ini…
An Artificial Neuron for Enhanced Problem Solving in Large Language Models
Sumedh Rasal
Recent advancements in artificial intelligence have propelled the capabilities of Large Language Models, yet their ability to mimic nuanced human reasoning remains limited. This pa…
Navigating Complexity: Orchestrated Problem Solving with Multi-Agent LLMs
Sumedh Rasal, E. J. Hauer
Large Language Models (LLMs) have demonstrated remarkable capabilities in solving various tasks, yet they often struggle with comprehensively addressing complex and vague problems.…
LLM Harmony: Multi-Agent Communication for Problem Solving
Sumedh Rasal
Large Language Models (LLMs) have revolutionized Natural Language Processing but exhibit limitations, particularly in autonomously addressing novel challenges such as reasoning and…
Beyond Segmentation: Road Network Generation with Multi-Modal LLMs
Sumedh Rasal, Sanjay Kumar Boddhu
This paper introduces an innovative approach to road network generation through the utilization of a multi-modal Large Language Model (LLM). Our model is specifically designed to p…