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- W4320342989 abstract "Recent work has addressed textual reasoning tasks by prompting large language models with explanations via the chain-of-thought paradigm. However, subtly different explanations can yield widely varying downstream task accuracy, so explanations that have not been tuned for a task, such as off-the-shelf explanations written by non-experts, may lead to mediocre performance. This paper tackles the problem of how to optimize explanation-infused prompts in a black-box fashion. We first generate sets of candidate explanations for each example in the prompt using a leave-one-out scheme. We then use a two-stage framework where we first evaluate explanations for each in-context example in isolation according to two proxy metrics, log likelihood and accuracy on new examples. Finally, we search over sets of explanations to find a set that yields high performance against a silver-labeled development set. Across four textual reasoning tasks spanning question answering, mathematical reasoning, and natural language inference, results show that our proxy metrics correlate with ground truth accuracy and our overall method can effectively improve prompts over crowdworker annotations and naive search strategies." @default.
- W4320342989 created "2023-02-13" @default.
- W4320342989 creator A5015133105 @default.
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- W4320342989 date "2023-02-09" @default.
- W4320342989 modified "2023-09-25" @default.
- W4320342989 title "Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting" @default.
- W4320342989 doi "https://doi.org/10.48550/arxiv.2302.04813" @default.
- W4320342989 hasPublicationYear "2023" @default.
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