Semantic retrieval in e-commerce search aims to identify a compact candidate set from billion-scale product catalogs with both high recall and low latency. Dual-Encoders dominate this stage due to their efficient dot-product similarity, but this formulation limits model expressiveness and fails to capture fine-grained...
Guo-Hao Tan, Jia-Hui Wan, Tao Wen et al.· Proceedings of the 20th ACM...· 0 citations
Long-form factuality verification is commonly implemented as a static decompose-search-verify pipeline, with separately prompted modules processing claims and invoking external search. Treating claims independently makes LLM and search calls scale with claim count and causes repeated searches for overlapping evidence a...
Ke-Ning Zheng, Ao-Ying Zheng, Zhi-Gang Chang et al.· 0 citations
This work proposes the Cluster-Ranked Identifier (CRID), which decouples DocID into semantic clustering and business-value ranking, yielding collision-free identifiers that support incremental updates via intra-cluster reranking.
Gui Ling, Zhihong Chen, Yu Li et al.· arXiv.org· 2 citations
RPS is proposed, a novel training-free decoding method that seeks mid-entropy positions as promising candidate pivots (where to decode), and determines their token assignment that yields the greatest downstream benefit via lookahead evaluation (what to decode).
Yu-Shi Ye, Xu Chen, Hao-Yun Jiang et al.· 1 citation
Generative retrieval has become an increasingly adopted paradigm for industrial search, recommendation, and advertising systems, delivering significant online gains. Most existing work combines user behavior sequences with large language models (LLMs) to model user preferences. In practice, feature engineering remains...
Dan Ou, Gui Ling, Haokai Wan et al.· 0 citations
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