CodecLM: Aligning Language Models with Tailored Synthetic Data

no code implementations • 8 Apr 2024 • Zifeng Wang, Chun-Liang Li, Vincent Perot, Long T. Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister

To this end, we introduce CodecLM, a general framework for adaptively generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs.

Instruction Following

Interpretable Sequence Learning for COVID-19 Forecasting

no code implementations • NeurIPS 2020 • Sercan O. Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long T. Le, Vikas Menon, Shashank Singh, Leyou Zhang, Nate Yoder, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister

We propose a novel approach that integrates machine learning into compartmental disease modeling to predict the progression of COVID-19.