DoubleDipper: Recycling Contexts for Efficient and Attributed In-Context Learning

AbstractIn this work, we propose DoubleDipper, a novel In-Context-Learning method that automatically generates few-shot examples for several QA tasks by _recycling_ contexts. Specifically, given an input context (1-3k tokens) and a query, we generate additional query-output pairs from the given context as few-shot examples, while introducing the context only once. This ensures that the demonstrations are leveraging the same context as the target query while only adding a small number of tokens to the prompt. We further enhance each demonstration by instructing the model to _explicitly_ identify the relevant paragraphs before the answer, which improves performance while providing fine-grained attribution to the answer source. We apply our method on multiple LLMs and obtain substantial improvements (+16 absolute points on average across models) on various QA datasets. Surprisingly, despite introducing only single-hop ICL examples, LLMs successfully generalize to multi-hop QA using our approach.