4 citations · 4 across the 1 of their papers we have counts for
4 papers
CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation
Junda Wu, Cheng-Chun Chang, Tong Yu +4
The long-tail recommendation is a challenging task for traditional recommender systems, due to data sparsity and data imbalance issues. The recent development of large language mod…
InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment
Jianing Wang, Junda Wu, Yupeng Hou +3
Do current large language models (LLMs) better solve graph reasoning and generation tasks with parameter updates? In this paper, we propose InstructGraph, a framework that empowers…
FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis
Ziao Wang, Yuhang Li, Junda Wu +2
In this paper, we propose FinVis-GPT, a novel multimodal large language model (LLM) specifically designed for financial chart analysis. By leveraging the power of LLMs and incorpor…
InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding
Junda Wu, Tong Yu, Rui Wang +6
Soft prompt tuning achieves superior performances across a wide range of few-shot tasks. However, the performances of prompt tuning can be highly sensitive to the initialization of…