Retrieval Augmented Thoughts (RAT)
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RAT augments chain-of-thought by iteratively rewriting each reasoning step using retrieved context, sharply reducing hallucination on long-horizon generation tasks.
Iterative thought revision: After producing a zero-shot CoT, the method walks step-by-step and rewrites each thought using retrieved info that depends on the query and the preceding thoughts.
Zero-shot and model-agnostic: RAT works without task-specific training and improves GPT-3.5, GPT-4, and CodeLLaMA-7B alike, generalizing across backbone sizes.
Large gains on long-horizon tasks: Average relative improvements of 13.6% (code), 17.0% (math), 19.2% (creative writing), and 42.8% (embodied planning) over zero-shot CoT and vanilla RAG baselines.
Implication: Retrieval is most useful when woven into reasoning at each step rather than bolted on up-front, especially as tasks demand longer, multi-step outputs.
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