4 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
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…
ALPS: Attention Localization and Pruning Strategy for Efficient Alignment of Large Language Models
Hao Chen, Haoze Li, Zhiqing Xiao +6
Aligning general-purpose large language models (LLMs) to downstream tasks often incurs significant training adjustment costs. Prior research has explored various avenues to enhance…
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…
TableGPT2: A Large Multimodal Model with Tabular Data Integration
Aofeng Su, Aowen Wang, Chao Ye +30
The emergence of models like GPTs, Claude, LLaMA, and Qwen has reshaped AI applications, presenting vast new opportunities across industries. Yet, the integration of tabular data r…