8 papers
Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learning
Jiahan Chen, Da Li, Hengran Zhang +6
Multimodal embedding models, rooted in multimodal large language models (MLLMs), have yielded significant performance improvements across diverse tasks such as retrieval and classi…
Large Language Model-Powered Query-Driven Event Timeline Summarization in Industrial Search
Mingyue Wang, Xingyu Xie, Hang Yang +5
Understanding how events evolve over time is essential for search engines handling queries about trending news. We present QDET (Query-Driven Event Timeline Summarization), a produ…
RAG-Enhanced Large Language Models for Dynamic Content Expiration Prediction in Web Search
Tingyu Chen, Wenkai Zhang, Li Gao +4
In commercial web search, aligning content freshness with user intent remains challenging due to the highly varied lifespans of information. Traditional industrial approaches rely…
How to Utilize Complementary Vision-Text Information for 2D Structure Understanding
Jiancheng Dong, Pengyue Jia, Derong Xu +9
LLMs typically linearize 2D tables into 1D sequences to fit their autoregressive architecture, which weakens row-column adjacency and other layout cues. In contrast, purely visual…
Towards AI Search Paradigm
Yuchen Li, Hengyi Cai, Rui Kong +20
In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-maki…
Behavior-Equivalent Token: Single-Token Replacement for Long Prompts in LLMs
Jiancheng Dong, Pengyue Jia, Jingyu Peng +7
Carefully engineered system prompts play a critical role in guiding the behavior of LLM agents, but their considerable length introduces significant drawbacks, including increased…