RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
<|reference_start|>RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation: 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<|reference_end|>
@article{wang2024rat:,
    title={RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in
  Long-Horizon Generation},
    author={Zihao Wang and Anji Liu and Haowei Lin and Jiaqi Li and Xiaojian Ma
  and Yitao Liang},
    journal={arXiv preprint arXiv:2403.05313},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05313},
    primaryClass={cs.CL cs.AI}
}