While chain-of-thought (CoT) prompting has revolutionized how LLMs perform
reasoning tasks, its current methods and variations (e.g, Self-consistency,
ReACT, Reflexion, Tree-of-Thoughts (ToT), Cumulative Reasoning (CR)) suffer
from limitations like slowness, limited context grounding, hallucination and
inconsistent outputs. To overcome these challenges, we introduce Evidence to
Generate (E2G), a novel single-agent, two-step prompting framework. Instead of
unverified reasoning claims, this innovative approach leverages the power of
"evidence for decision making" by first focusing exclusively on the thought
sequences (the series of intermediate steps) explicitly mentioned in the
context which then serve as extracted evidence, guiding the LLM's output
generation process with greater precision and efficiency. This simple yet
powerful approach unlocks the true potential of chain-of-thought like
prompting, paving the way for faster, more reliable, and more contextually
aware reasoning in LLMs. \tool achieves remarkable results robustly across a
wide range of knowledge-intensive reasoning and generation tasks, surpassing
baseline approaches with state-of-the-art LLMs. For example, (i) on LogiQA
benchmark using GPT-4 as backbone model, \tool achieves a new state-of-the
Accuracy of 53.8% exceeding CoT by 18%, ToT by 11%, CR by 9% (ii) a variant of
E2G with PaLM2 outperforms the variable-shot performance of Gemini Ultra by 0.9
F1 points, reaching an F1 score of 83.3 on a subset of DROP.

通过引入 E2G 方法，基于证据的推理框架，可以解决现有的 CoT 提示方法的局限性，提高 LLM 推理过程的准确性和效率。该方法在知识密集型任务中取得了显著结果，优于基准方法和现有的 LLM 模型。