11 papers
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…
Less Is More: Elevating RAG via Performance-Driven Context Compression
Ziqiang Cui, Yunpeng Weng, Xing Tang +7
Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm for improving the timeliness of knowledge updates and the factual accuracy of large language models. Howeve…
Looking Farther with Confidence: Uncertainty-Guided Future Learning for Sequential Recommendation
Ziqiang Cui, Xing Tang, Peiyang Liu +4
Sequential recommendation effectively models dynamic user interests but continues to face challenges related to data sparsity. While self-supervised learning has alleviated this is…
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…
Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation
Xing Tang, Jingyang Bin, Ziqiang Cui +6
The sequential recommendation (SR) task aims to predict the next item based on users' historical interaction sequences. Typically trained on historical data, SR models often strugg…
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…