Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming (2023.findings-acl)
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| Challenge: | Pre-trained large language models struggle to perform logical reasoning reliably despite advances in scale and compositionality. |
| Approach: | They propose a Differentiable Symbolic Reasoning framework that uses symbolic programming to improve LMs' logical reasoning abilities. |
| Outcome: | The proposed framework outperforms competitive baselines when faced with systematic changes in sequence length. |
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