emnlp2024-code-prompting
Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs. EMNLP 2024
We propose a chain of prompts that transforms a natural language problem into code and prompts the LLM with the generated code. We conduct experiments across two datasets: ConditionalQA, a scenario-based question answering (QA) dataset and BoardgameQA, a boardgame-based QA dataset with conflicting rules. Code prompts achieve large gains compared to text prompts. We also observe that code prompts are more efficient, requiring fewer demonstrations, and that they trigger superior state tracking of variables or key entities.
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