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cs.IR2026
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…
cs.IR2026
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…
cs.IR2026
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…