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.
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Python packages can be installed with
pip install -r requirements.txt -
OpenAI_API_keyis required for the language model. You can get it from OpenAI.
export OPENAI_API_KEY="sk-******"
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You can also use the Huggingface API key for the language model. You can get it from Huggingface.
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You also need to prepare the
GOOGLE_API_KEYfor Google Search API. You can get it from Google Cloud Platform.
export GOOGLE_API_KEY="********"
You can run the ipython notebook named creative.ipynb to generate open-ended creative text with RAT.
You can also use the following code to start a Gradio interface to interact with RAT:
python app/gradio_app.pyApplying RAT to various base models 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.
Our paper is available on Arxiv. Please cite our paper if you find RAT useful for your research:
@article{wang2024rat,
author = {Zihao, Wang and Anji, Liu and Haowei, Lin and Jiaqi, Li and Xiaojian, Ma and Yitao, Liang},
title = {RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation},
journal = {arXiv preprint arXiv: 2403.05313},
year = {2024},
}