5 papers
Generative Universal Multimodal Retrieval with Dual-role Identifiers
Kaipeng Li, Haitao Yu, Xuanchen Zhou
Generative information retrieval (GIR) has emerged as a compelling alternative to the conventional index-retrieve-then-rank retrieval pipeline by training a generator to produce th…
LoReC: Rethinking Large Language Models for Graph Data Analysis
Hongyu Zhan, Qixin Wang, Yusen Tan +6
The advent of Large Language Models (LLMs) has fundamentally reshaped the way we interact with graphs, giving rise to a new paradigm called GraphLLM. As revealed in recent studies,…
Event-Centric Human Value Understanding in News-Domain Texts: An Actor-Conditioned, Multi-Granularity Benchmark
Yao Wang, Xin Liu, Zhuochen Liu +5
Existing human value datasets do not directly support value understanding in factual news: many are actor-agnostic, rely on isolated utterances or synthetic scenarios, and lack exp…
The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
Junchen Fu, Xuri Ge, Xin Xin +5
Multimodal representation learning has garnered significant attention in the AI community, largely due to the success of large pre-trained multimodal foundation models like LLaMA,…
R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation
Zihan Wang, Xuri Ge, Joemon M. Jose +4
Retrieval-augmented generation (RAG) has gained wide attention as the key component to improve generative models with external knowledge augmentation from information retrieval. It…