4 citations · 10 across the 17 of their papers we have counts for
19 papers
HART: Hybrid Attention-RWKV Time series Foundation Model
Qingwen Lin, Boyan Xu, Shifeng Xie +3
Non-autoregressive multi-step forecasting (NAF) enables time series foundation models to predict an entire future horizon in a single forward pass. Despite its strong sequence mode…
InstMoE: Adaptive Multimodal Routing with Specialized Experts
Guimin Hu, Xiang He, Yingjian Li +3
Multimodal inputs are inherently heterogeneous, not only across modalities but also in the information pathways required for effective prediction. To address this limitation, we pr…
SANE: State Anomaly Neutralization for Stable Extreme-Context Delta-Rule Models
Qingwen Lin, Boyan Xu, Xiao Liu +2
Delta-Rule recurrent models maintain a fixed-size state, enabling inference memory but potentially becoming unstable under extreme-context extrapolation. By tracking RWKV-7…
EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis
Lei Zan, Keli Zhang, Shifeng Xie +7
Modern telecommunication, cloud, and microservice systems emit correlated alarm cascades when components fail. Root cause analysis (RCA) aims to identify the small set of alarms th…
SAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition
Ruichu Cai, Juntao Gan, Miao Mai +2
Zero-shot Named Entity Recognition (ZS-NER) remains brittle under domain and schema shifts, where unseen label definitions often misalign with a large language model's (LLM's) intr…
SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification
Zhifeng Hao, Zhongjie Chen, Junhao Lu +5
Event Causality Identification (ECI) requires models to determine whether a given pair of events in a context exhibits a causal relationship. While Large Language Models (LLMs) hav…
IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction
Bingfeng Chen, Chenjie Qiu, Yifeng Xie +3
Aspect Sentiment Quad Prediction (ASQP) has seen significant advancements, largely driven by the powerful semantic understanding and generative capabilities of large language model…
Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQL
Bingfeng Chen, Shaobin Shi, Yongqi Luo +3
Generative language models have shown significant potential in single-turn Text-to-SQL. However, their performance does not extend equivalently to multi-turn Text-to-SQL. This is p…
Hierarchical Action Learning for Weakly-Supervised Action Segmentation
Junxian Huang, Ruichu Cai, Hao Zhu +5
Humans perceive actions through key transitions that structure actions across multiple abstraction levels, whereas machines, relying on visual features, tend to over-segment. This…
What Gets Activated: Uncovering Domain and Driver Experts in MoE Language Models
Guimin Hu, Meng Li, Qiwei Peng +3
Most interpretability work focuses on layer- or neuron-level mechanisms in Transformers, leaving expert-level behavior in MoE LLMs underexplored. Motivated by functional specializa…
ENTRA: Entropy-Based Redundancy Avoidance in Large Language Model Reasoning
Ruichu Cai, Haopeng Du, Qingwen Lin +3
Large Reasoning Models (LRMs) often suffer from overthinking, generating unnecessarily long reasoning chains even for simple tasks. This leads to substantial computational overhead…
Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation
Zhifeng Hao, Qibin Song, Ruichu Cai +1
Recent divide-and-conquer reasoning approaches, particularly those based on Chain-of-Thought (CoT), have substantially improved the Text-to-SQL capabilities of Large Language Model…
CAMA: Enhancing Mathematical Reasoning in Large Language Models with Causal Knowledge
Lei Zan, Keli Zhang, Ruichu Cai +1
Large Language Models (LLMs) have demonstrated strong performance across a wide range of tasks, yet they still struggle with complex mathematical reasoning, a challenge fundamental…
Causal-aware Large Language Models: Enhancing Decision-Making Through Learning, Adapting and Acting
Wei Chen, Jiahao Zhang, Haipeng Zhu +5
Large language models (LLMs) have shown great potential in decision-making due to the vast amount of knowledge stored within the models. However, these pre-trained models are prone…
CMCTS: A Constrained Monte Carlo Tree Search Framework for Mathematical Reasoning in Large Language Model
Qingwen Lin, Boyan Xu, Guimin Hu +4
This paper introduces the Constrained Monte Carlo Tree Search (CMCTS) framework to enhance the mathematical reasoning capabilities of Large Language Models (LLM). By incorporating…
TP-UNet: Temporal Prompt Guided UNet for Medical Image Segmentation
Ranmin Wang, Limin Zhuang, Hongkun Chen +2
The advancement of medical image segmentation techniques has been propelled by the adoption of deep learning techniques, particularly UNet-based approaches, which exploit semantic…
Recent Advances in Multimodal Affective Computing: An NLP Perspective
Guimin Hu, Weimin Lyu, Chang Sun +5
Multimodal affective computing has gained increasing attention due to its broad applications in understanding human behavior and intentions, particularly in text-centric multimodal…
SGSL: Incorporating Segment to Syntactic Enhanced Graph Structure Learning for Aspect-based Sentiment Analysis
Bingfeng Chen, Qihan Ouyang, Yongqi Luo +3
Previous graph-based approaches in Aspect based Sentiment Analysis(ABSA) have demonstrated impressive performance by utilizing graph neural networks and attention mechanisms to lea…
From Large to Tiny: Distilling and Refining Mathematical Expertise for Math Word Problems with Weakly Supervision
Qingwen Lin, Boyan Xu, Zhengting Huang +1
Addressing the challenge of high annotation costs in solving Math Word Problems (MWPs) through full supervision with intermediate equations, recent works have proposed weakly super…