3 citations · 4 across the 7 of their papers we have counts for
7 papers
Goal-Conditioned Supervised Learning for LLM Fine-Tuning
Shijun Li, Kaiwen Dong, Xiang Gao +1
Large language models often require fine-tuning to better align their behavior with user intent at deployment. Existing approaches are commonly divided into online and offline para…
RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
Shijun Li, Wooseong Yang, Pranav Belligundu +3
Large Language Models (LLMs) have emerged as a promising paradigm for next-generation recommender systems, offering strong semantic understanding and natural-language reasoning abi…
LLM Reasoning for Cold-Start Item Recommendation
Shijun Li, Yu Wang, Jin Wang +3
Large Language Models (LLMs) have shown significant potential for improving recommendation systems through their inherent reasoning capabilities and extensive knowledge base. Yet,…
OMAC: A Holistic Optimization Framework for LLM-Based Multi-Agent Collaboration
Shijun Li, Hilaf Hasson, Joydeep Ghosh
Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications. Recently, Multi-Agent Systems (MAS), wherein…
Goal-Conditioned Supervised Learning for Multi-Objective Recommendation
Shijun Li, Hilaf Hasson, Jing Hu +1
Multi-objective learning endeavors to concurrently optimize multiple objectives using a single model, aiming to achieve high and balanced performance across diverse objectives. How…
SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
Vijay Lingam, Atula Tejaswi, Aditya Vavre +7
Popular parameter-efficient fine-tuning (PEFT) methods, such as LoRA and its variants, freeze pre-trained model weights \(W\) and inject learnable matrices \(ΔW\). These \(ΔW\) mat…
Exploring Explainability in Video Action Recognition
Avinab Saha, Shashank Gupta, Sravan Kumar Ankireddy +2
Image Classification and Video Action Recognition are perhaps the two most foundational tasks in computer vision. Consequently, explaining the inner workings of trained deep neural…