21 citations · 25 across the 6 of their papers we have counts for
6 papers
TKG-Thinker: Towards Dynamic Reasoning over Temporal Knowledge Graphs via Agentic Reinforcement Learning
Zihao Jiang, Miao Peng, Zhenyan Shan +5
Temporal knowledge graph question answering (TKGQA) aims to answer time-sensitive questions by leveraging temporal knowledge bases. While Large Language Models (LLMs) demonstrate s…
Towards Explainable Temporal Reasoning in Large Language Models: A Structure-Aware Generative Framework
Zihao Jiang, Ben Liu, Miao Peng +4
While large language models (LLMs) show great potential in temporal reasoning, most existing work focuses heavily on enhancing performance, often neglecting the explainable reasoni…
Retrieval-augmented Large Language Models for Financial Time Series Forecasting
Mengxi Xiao, Zihao Jiang, Lingfei Qian +10
Accurately forecasting stock price movements is critical for informed financial decision-making, supporting applications ranging from algorithmic trading to risk management. Howeve…
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
Jimin Huang, Mengxi Xiao, Dong Li +41
Financial LLMs hold promise for advancing financial tasks and domain-specific applications. However, they are limited by scarce corpora, weak multimodal capabilities, and narrow ev…
Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning
Miao Peng, Ben Liu, Wenjie Xu +3
Temporal Knowledge Graph Reasoning (TKGR) is the task of inferring missing facts for incomplete TKGs in complex scenarios (e.g., transductive and inductive settings), which has bee…
Towards the Generalization of Contrastive Self-Supervised Learning
Weiran Huang, Mingyang Yi, Xuyang Zhao +1
Recently, self-supervised learning has attracted great attention, since it only requires unlabeled data for model training. Contrastive learning is one popular method for self-supe…