Uniting Large Language Models and Decision Trees
Abstract: Decision trees are a pillar of modern machine learning, forming a foundation for transparent, accurate decision making. However, decision trees often fail to model complex interactions, an area where modern large language models (LLMs) excel. In this talk, I will discuss our recent works that unite decision trees and LLMs to bring out the best in both for NLP applications. Specifically, I will discuss (1) how LLMs can be used to improve transparent decision trees by augmenting individual nodes with relevant features and (2) how decision trees can be used to steer LLMs by structuring sequential prompted calls.
Bio: Chandan is a senior researcher at Microsoft Research, where he works on interpretable machine learning with the broad goal of improving science and medicine using data. Recently, he has focused on language models and how they can be used to directly explain data or to improve transparent models. He completed his Computer Science PhD from UC Berkeley in 2022.