  Despite their impressive capabilities, large language models (LLMs) often
face challenges such as temporal misalignment and generating hallucinatory
content. Enhancing LLMs with retrieval mechanisms to fetch relevant information
from external sources offers a promising solution. Inspired by the proverb
"Think twice before you act," we propose a dual-angle evaluated
retrieval-augmented generation framework \textit{Think-then-Act}. Unlike
previous approaches that indiscriminately rewrite queries or perform retrieval
regardless of necessity, or generate temporary responses before deciding on
additional retrieval, which increases model generation costs, our framework
employs a two-phase process: (i) assessing the input query for clarity and
completeness to determine if rewriting is necessary; and (ii) evaluating the
model's capability to answer the query and deciding if additional retrieval is
needed. Experimental results on five datasets show that the
\textit{Think-then-Act} framework significantly improves performance. Our
framework demonstrates notable improvements in accuracy and efficiency compared
to existing baselines and performs well in both English and non-English
contexts. Ablation studies validate the optimal model confidence threshold,
highlighting the resource optimization benefits of our approach.
