7 papers
TabDLM: Free-Form Tabular Data Generation via Joint Numerical-Language Diffusion
Donghong Cai, Jiarui Feng, Yanbo Wang +3
Synthetic tabular data generation has attracted growing attention due to its importance for data augmentation, foundation models, and privacy. However, real-world tabular datasets…
Optimsyn: Influence-Guided Rubrics Optimization for Synthetic Data Generation
Zhiting Fan, Ruizhe Chen, Tianxiang Hu +7
Large language models (LLMs) achieve strong downstream performance largely due to abundant supervised fine-tuning (SFT) data. However, high-quality SFT data in knowledge-intensive…
GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models
Jiarui Feng, Donghong Cai, Yixin Chen +1
Large Language Models (LLMs) have demonstrated remarkable capabilities in modeling sequential textual data and generalizing across diverse tasks. However, effectively adapting LLMs…
Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs
Lecheng Kong, Xiyuan Wang, Yixin Chen +1
Large Language Models (LLMs) are emerging as versatile foundation models for computational chemistry, handling bidirectional tasks like reaction prediction and retrosynthesis. Howe…
Addressing accuracy and hallucination of LLMs in Alzheimer's disease research through knowledge graphs
Tingxuan Xu, Jiarui Feng, Justin Melendez +6
In the past two years, large language model (LLM)-based chatbots, such as ChatGPT, have revolutionized various domains by enabling diverse task completion and question-answering ca…
GOFA: A Generative One-For-All Model for Joint Graph Language Modeling
Lecheng Kong, Jiarui Feng, Hao Liu +4
Foundation models, such as Large Language Models (LLMs) or Large Vision Models (LVMs), have emerged as one of the most powerful tools in the respective fields. However, unlike text…