6 papers
SAILRec: Steering LLM Attention to Dual-Side Semantically Aligned Collaborative Embeddings for Recommendation
Xi Wu, Jiale Wang, Zihan Wang +5
Recent LLM-based recommenders enhance language models with collaborative embeddings from user-item interactions, but making such embeddings available does not ensure their proper u…
From Parameter Dynamics to Risk Scoring : Quantifying Sample-Level Safety Degradation in LLM Fine-tuning
Xiao Wang, Yifei Zhang, YongKang Liu +4
Safety alignment of Large Language Models (LLMs) is extremely fragile, as fine-tuning on a small number of benign samples can erase safety behaviors learned from millions of prefer…
DEEPMED: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference
Zihan Wang, Hao Wang, Shi Feng +6
Medical reasoning models remain constrained by parametric knowledge and are thus susceptible to forgetting and hallucinations. DeepResearch (DR) models ground outputs in verifiable…
CIRAG: Construction-Integration Retrieval and Adaptive Generation for Multi-hop Question Answering
Zili Wei, Xiaocui Yang, Yilin Wang +5
Triple-based Iterative Retrieval-Augmented Generation (iRAG) mitigates document-level noise for multi-hop question answering. However, existing methods still face limitations: (i)…
Muse: A Multimodal Conversational Recommendation Dataset with Scenario-Grounded User Profiles
Zihan Wang, Xiaocui Yang, Yongkang Liu +3
Current conversational recommendation systems focus predominantly on text. However, real-world recommendation settings are generally multimodal, causing a significant gap between e…
Enhancing LLM-based Recommendation through Semantic-Aligned Collaborative Knowledge
Zihan Wang, Jinghao Lin, Xiaocui Yang +4
Large Language Models (LLMs) demonstrate remarkable capabilities in leveraging comprehensive world knowledge and sophisticated reasoning mechanisms for recommendation tasks. Howeve…