Dzung Phan, Vinicius Lima
INFORMS 2023
Chain-of-thought (CoT) reasoning applies to complex tasks with multiple intermediate steps, a key feature of large language models. Recent studies have revealed CoT as a composition of in-context filtering and learning. This paper proposes a unified framework for CoT optimization that exploits the nested problem structure to formulate training as multilevel optimization. Each intermediate reasoning step is a distinct optimization level. We develop an epigraph-based multilevel optimization (EMO) method to iteratively find the optimal solution for this class of problems. Experiments using GPT-2 show that the proposed EMO achieves the lowest generalization errors across all intermediate steps compared to state-of-the-art, highlighting the importance of nested optimization approaches for CoT reasoning.
Dzung Phan, Vinicius Lima
INFORMS 2023
Jehanzeb Mirza, Leonid Karlinsky, et al.
NeurIPS 2023
Hagen Soltau, Lidia Mangu, et al.
ASRU 2011
Liya Fan, Fa Zhang, et al.
JPDC