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- W4386908508 abstract "How can we perform computations over natural language representations to solve tasks that require symbolic and numeric reasoning? We propose natural language embedded programs (NLEP) as a unifying framework for addressing math/symbolic reasoning, natural language understanding, and instruction following tasks. Our approach prompts a language model to generate full Python programs that define functions over data structures which contain natural language representations of structured knowledge. A Python interpreter then executes the generated code and prints the output. Despite using a task-general prompt, we find that this approach can improve upon strong baselines across a range of different tasks including math and symbolic reasoning, text classification, question answering, and instruction following. We further find the generated programs are often interpretable and enable post-hoc verification of the intermediate reasoning steps." @default.
- W4386908508 created "2023-09-21" @default.
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- W4386908508 date "2023-09-19" @default.
- W4386908508 modified "2023-10-08" @default.
- W4386908508 title "Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning" @default.
- W4386908508 doi "https://doi.org/10.48550/arxiv.2309.10814" @default.
- W4386908508 hasPublicationYear "2023" @default.
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