LLM

Overview and Motivation Presentation on integrating reinforcement learning concepts with LLM engines to improve performance Multiple approaches exist to enhance LLM performance: in-context learning, post-training (reinforcement learning), and fine-tuning Focus on efficiently integrating RL concepts with LLMs, particularly for multi-agent systems Covers differences between reinforcement learning on LLMs versus reinforcement learning on LLM agents Addresses RL implementation in HPC systems due to memory-intensive requirements Multi-Agent System Fundamentals Multi-agent systems consist of multiple LLMs, each with specific roles and functions State acts as history, compiling all previous agent turns, context, and evidence in multi-turn systems Communication Topologies ...

Overview On March 28, 2025, Miftahul Jannat Mokarrama, a PhD student in the Computer Science department, presented a research topic on “LLMs as Judges,” a trending subject in the field of large language models. Her presentation provided a comprehensive survey covering functionality, methodology, applications, meta-evaluation, and limitations. Key conclusions included: LLMs as evaluators are versatile, evaluation is context-specific, challenges persist, human-AI collaboration is essential, and evaluation should extend beyond traditional research papers.

Overview On 02/21/2025, the first NIU AI/ML Research Seminar was held in PM103. Ibrahim Al Azher, a PhD student from the CS department, delivered a general introduction to current studies on Large Language Models, their limitations, and recent advancements in Retrieval-Augmented Generation (RAG). The presentation, which lasted about an hour, was well received by the participants and sparked engaging discussions afterward. This event marked a successful start to the AI/ML research seminar series.