ARR: Question Answering with Large Language Models via Analyzing,
  Retrieving, and Reasoning
Authors: name: Yuwei Yin, name: Giuseppe Carenini
Organization: University of British Columbia

Large language models (LLMs) achieve remarkable performance on challenging
benchmarks that are often structured as multiple-choice question-answering (QA)
tasks. Zero-shot Chain-of-Thought (CoT) prompting enhances reasoning in LLMs
but provides only vague and generic guidance ("think step by step"). This paper
introduces ARR, an intuitive and effective zero-shot prompting method that
explicitly incorporates three key steps in QA solving: analyzing the intent of
the question, retrieving relevant information, and reasoning step by step.
Comprehensive experiments across diverse and challenging QA tasks demonstrate
that ARR consistently improves the Baseline (without ARR prompting) and
outperforms CoT. Ablation and case studies further validate the positive
contributions of each component: analyzing, retrieving, and reasoning. Notably,
intent analysis plays a vital role in ARR. Additionally, extensive evaluations
across various model sizes, LLM series, and generation settings solidify the
effectiveness, robustness, and generalizability of ARR.