QIN: Yeah, uh, we work on the problem of molecular docking, a computational modeling method used to predict the preferred orientation of one molecule when it binds to a second molecule to form a stable complex. So it aims to predict the binding pose of a ligand in the active site of a receptor and estimate the ligand-receptor binding affinity. This problem is very important for drug discovery and development. Accurately predicting binding poses can provide insights into how a […]

We’re seeing exciting capabilities of frontier foundation models, including intriguing powers of abstraction, generalization, and composition across numerous areas of knowledge and expertise. Even seasoned AI researchers have been impressed with the ability to steer the models with straightforward, zero-shot prompts. Beyond basic, out-of-the-box prompting, we’ve been exploring new prompting strategies, showcased in our Medprompt work, to evoke the powers of specialists.   Today, we’re sharing information on Medprompt and other approaches to steering frontier models in

Contributors Marah Abdin, Jyoti Aneja, Sebastien Bubeck, Caio César Teodoro Mendes, Weizhu Chen, Allie Del Giorno, Ronen Eldan, Sivakanth Gopi, Suriya Gunasekar, Mojan Javaheripi, Piero Kauffmann, Yin Tat Lee, Yuanzhi Li, Anh Nguyen, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Michael Santacroce, Harkirat Singh Behl, Adam Taumann Kalai, Xin Wang, Rachel Ward, Philipp Witte, Cyril Zhang, Yi Zhang

ALESSANDRO SORDONI: Hi, Gretchen, thank you for having me. HUIZINGA: So in a few sentences, tell us about the issue or problem that your research addresses and why we should care about it. SORDONI: So in this paper, our starting points are large language models, and to make large language models solve tasks, one of the ways that is currently used is to prompt them. By prompting that means just giving instruction to them, and hopefully by joining instruction and […]