7 citations · 8 across the 12 of their papers we have counts for
6 papers · 1 filter
Fighting Numerical Hallucinations via Data-centric Compilation for Online Financial QA
Hao Chen, Xing Tang, Qirui Liu +6
Large Language Models (LLMs) have significantly advanced online data services, particularly in the domain of financial question answering (FinQA). However, such systems remain susc…
Data-Driven Function Calling Improvements in Large Language Model for Online Financial QA
Xing Tang, Hao Chen, Shiwei Li +7
Large language models (LLMs) have been incorporated into numerous industrial applications. Meanwhile, a vast array of API assets is scattered across various functions in the financ…
Automated Information Flow Selection for Multi-scenario Multi-task Recommendation
Chaohua Yang, Dugang Liu, Shiwei Li +6
Multi-scenario multi-task recommendation (MSMTR) systems must address recommendation demands across diverse scenarios while simultaneously optimizing multiple objectives, such as c…
BlossomRec: Block-level Fused Sparse Attention Mechanism for Sequential Recommendations
Mengyang Ma, Xiaopeng Li, Wanyu Wang +9
Transformer structures have been widely used in sequential recommender systems (SRS). However, as user interaction histories increase, computational time and memory requirements al…
Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation
Ziqiang Cui, Yunpeng Weng, Xing Tang +8
Contrastive learning has shown effectiveness in improving sequential recommendation models. However, existing methods still face challenges in generating high-quality contrastive p…
Mixed-Precision Embeddings for Large-Scale Recommendation Models
Shiwei Li, Zhuoqi Hu, Xing Tang +6
Embedding techniques have become essential components of large databases in the deep learning era. By encoding discrete entities, such as words, items, or graph nodes, into continu…