13 papers
A/B Agent: A Self-Evolving Agent for Strategy Iteration in Industrial A/B Testing
Zhuohang Jiang, Yuxin Chen, Yongsen Pan +6
Industrial recommendation strategy iteration heavily relies on large-scale A/B experimentation. Traditional tuning requires experts to repeatedly design strategies, configure exper…
HVM-GraphRAG: Holistic-View Multimodal Graph Retrieval-Augmented Generation on Complex Document
Xin He, Yili Wang, Wenqi Fan +4
Question answering (QA) over complex documents requires models to retrieve and integrate evidence distributed across distant document regions and modalities. Multimodal GraphRAG pr…
mKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQA
Xu Yuan, Liangbo Ning, Qingqing Ye +2
Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for expanding the knowledge capacity of Multimodal Large Language Models (MLLMs) by incorporating external…
ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning
Jiani Huang, Shijie Wang, Liangbo Ning +2
With the rise of LLMs, there is an increasing need for intelligent recommendation assistants that can handle complex queries and provide personalized, reasoning-driven recommendati…
QA-Dragon: Query-Aware Dynamic RAG System for Knowledge-Intensive Visual Question Answering
Zhuohang Jiang, Pangjing Wu, Xu Yuan +2
Retrieval-Augmented Generation (RAG) has been introduced to mitigate hallucinations in Multimodal Large Language Models (MLLMs) by incorporating external knowledge into the generat…
WebRec: Enhancing LLM-based Recommendations with Attention-guided RAG from Web
Zihuai Zhao, Yujuan Ding, Wenqi Fan +1
Recommender systems play a vital role in alleviating information overload and enriching users' online experience. In the era of large language models (LLMs), LLM-based recommender…