11 citations · 20 across the 27 of their papers we have counts for
6 papers · 1 filter
Multi-refined Feature Enhanced Sentiment Analysis Using Contextual Instruction
Peter Atandoh, Jie Zou, Weikang Guo +2
Sentiment analysis using deep learning and pre-trained language models (PLMs) has gained significant traction due to their ability to capture rich contextual representations. Howev…
MM-R1: Unleashing the Power of Unified Multimodal Large Language Models for Personalized Image Generation
Qian Liang, Yujia Wu, Kuncheng Li +4
Multimodal Large Language Models (MLLMs) with unified architectures excel across a wide range of vision-language tasks, yet aligning them with personalized image generation remains…
Pushing the Limits of Safety: A Technical Report on the ATLAS Challenge 2025
Zonghao Ying, Siyang Wu, Run Hao +44
Multimodal Large Language Models (MLLMs) have enabled transformative advancements across diverse applications but remain susceptible to safety threats, especially jailbreak attacks…
Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation
Guojia An, Jie Zou, Jiwei Wei +3
Conversational recommender systems aim to provide personalized recommendations by analyzing and utilizing contextual information related to dialogue. However, existing methods typi…
Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts
Jie Zou, Cheng Lin, Weikang Guo +4
Conversational recommender systems enable natural language conversations and thus lead to a more engaging and effective recommendation scenario. As the conversations for recommende…
Syzygy of Thoughts: Improving LLM CoT with the Minimal Free Resolution
Chenghao Li, Chaoning Zhang, Yi Lu +7
Chain-of-Thought (CoT) prompting enhances the reasoning of large language models (LLMs) by decomposing problems into sequential steps, mimicking human logic and reducing errors. Ho…