Talk details:

AI Seminar: Active Learning For Human Preference Elicitation In LLMs

Speaker:  Branislav Kveton, Adobe Research

Location: KEC 1001 and Zoom

Zoom link: https://oregonstate.zoom.us/s/98357211915

Talk Abstract:

Learning from human preferences has been central to recent advances in AI. Motivated by the cost of obtaining high-quality human feedback, we study active learning of large language models (LLMs) through adaptive human preference elicitation. The key idea in our work is to linearize LLMs, either the reward model or the optimized policy, and apply optimal designs, a methodology for computing optimal information-gathering policies in statistics. We design efficient algorithms, fundamentally leverage the autoregressive nature of the LLMs, and evaluate our algorithms on state-of-the-art models.

Speaker Bio:

Branislav (Brano) is a Principal Research Scientist at Adobe Research and leads their team on planning and reasoning with generative AI models. He proposes, analyzes, and applies algorithms that learn incrementally, run in real time, and converge to near-optimal solutions with more feedback. Brano has been an active researcher in the fields of ML and AI for more than 15 years. His works have been published at top-tier conferences in these fields and he serves on senior roles at these conferences.

For future AI seminars, please visit: https://engineering.oregonstate.edu/EECS/research/AI-seminars.

Best,

Christian Abou Mrad 

Graduate Student 

Oregon State University