407 citations · 489 across the 48 of their papers we have counts for
11 papers · 1 filter
TableGPT-R1: Advancing Tabular Reasoning Through Reinforcement Learning
Saisai Yang, Qingyi Huang, Jing Yuan +13
Tabular data serves as the backbone of modern data analysis and scientific research. While Large Language Models (LLMs) fine-tuned via Supervised Fine-Tuning (SFT) have significant…
TraPO: A Semi-Supervised Reinforcement Learning Framework for Boosting LLM Reasoning
Shenzhi Yang, Guangcheng Zhu, Xing Zheng +7
Reinforcement learning with verifiable rewards (RLVR) has proven effective in training large reasoning models (LRMs) by leveraging answer-verifiable signals to guide policy optimiz…
An Invariant Latent Space Perspective on Language Model Inversion
Wentao Ye, Jiaqi Hu, Haobo Wang +7
Language model inversion (LMI), i.e., recovering hidden prompts from outputs, emerges as a concrete threat to user privacy and system security. We recast LMI as reusing the LLM's o…
CYCLE-INSTRUCT: Fully Seed-Free Instruction Tuning via Dual Self-Training and Cycle Consistency
Zhanming Shen, Hao Chen, Yulei Tang +6
Instruction tuning is vital for aligning large language models (LLMs) with human intent, but current methods typically rely on costly human-annotated seed data or powerful external…
SPA++: Generalized Graph Spectral Alignment for Versatile Domain Adaptation
Zhiqing Xiao, Haobo Wang, Xu Lu +3
Domain Adaptation (DA) aims to transfer knowledge from a labeled source domain to an unlabeled or sparsely labeled target domain under domain shifts. Most prior works focus on capt…
Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligence
Jiaming Tian, Liyao Li, Wentao Ye +6
Tables are fundamental in domains such as finance, healthcare, and public administration, yet real-world table tasks often involve noise, structural heterogeneity, and semantic com…
RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis
Pengzuo Wu, Yuhang Yang, Guangcheng Zhu +10
With the rapid advancement of Large Language Models (LLMs), there is an increasing need for challenging benchmarks to evaluate their capabilities in handling complex tabular data.…
FLoE: Fisher-Based Layer Selection for Efficient Sparse Adaptation of Low-Rank Experts
Xinyi Wang, Lirong Gao, Haobo Wang +2
Parameter-Efficient Fine-Tuning (PEFT) methods have emerged as a widely adopted strategy for adapting pre-trained Large Language Models (LLMs) to downstream tasks, significantly re…
LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization
Qi Zhang, Shouqing Yang, Lirong Gao +8
Large language models (LLMs) have demonstrated impressive capabilities in reasoning with the emergence of reasoning models like OpenAI-o1 and DeepSeek-R1. Recent research focuses o…
Harnessing Feature Resonance under Arbitrary Target Alignment for Out-of-Distribution Node Detection
Shenzhi Yang, Junbo Zhao, Sharon Li +4
Detecting out-of-distribution (OOD) nodes in the graph-based machine-learning field is challenging, particularly when in-distribution (ID) node multi-category labels are unavailabl…
Towards Robust Incremental Learning under Ambiguous Supervision
Rui Wang, Mingxuan Xia, Chang Yao +4
Traditional Incremental Learning (IL) targets to handle sequential fully-supervised learning problems where novel classes emerge from time to time. However, due to inherent annotat…