DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning

Understand

Recent advances in natural language processing, primarily propelled by Large Language Models (LLMs), have showcased their remarkable capabilities grounded in in-context learning.

• A promising avenue for guiding LLMs in intricate reasoning tasks involves the utilization of intermediate reasoning steps within the Chain-of-Thought (CoT) paradigm.
• Nevertheless, the central challenge lies in the effective selection of exemplars for facilitating in-context learning.
• In this study, we introduce a framework that leverages Dual Queries and Low-rank approximation Re-ranking (DQ-LoRe) to automatically select exemplars for in-context learning.