RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in
  Long-Horizon Generation
Zihao Wang Anji Liu Haowei Lin Jiaqi Li Xiaojian Ma Yitao Liang
We explore how iterative revising a chain of thoughts with the help of
information retrieval significantly improves large language models' reasoning
and generation ability in long-horizon generation tasks, while hugely
mitigating hallucination. In particular, the proposed method --
*retrieval-augmented thoughts* (RAT) -- revises each thought step one by one
with retrieved information relevant to the task query, the current and the past
thought steps, after the initial zero-shot CoT is generated. Applying RAT to
GPT-3.5, GPT-4, and CodeLLaMA-7b substantially improves their performances on
various long-horizon generation tasks; on average of relatively increasing
rating scores by 13.63% on code generation, 16.96% on mathematical reasoning,
19.2% on creative writing, and 42.78% on embodied task planning. The demo page
can be found at https://craftjarvis.github.io/RAT