Papers with core selection

1 papers
Explicit Planning Helps Language Models in Logical Reasoning (2023.emnlp-main)

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Challenge: Existing systems that use pre-trained large language models to perform multi-step logical reasoning have been unable to perform this task.
Approach: They propose a system that uses language models to perform multi-step logical reasoning and incorporates explicit planning into the inference procedure.
Outcome: The proposed system outperforms other competing methods on multiple datasets and significantly outperformed chain-of-thought prompting on the PrOntoQA dataset.

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