LAMBADA: Backward Chaining for Automated Reasoning in Natural Language (2023.acl-long)
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| Challenge: | Recent advances in automated reasoning with natural text suffer from a combinatorial explosion of the search space and high failure rates for problems requiring longer chains of reasoning. |
| Approach: | They propose a Backward Chaining algorithm that decomposes reasoning into four sub-modules and implements it by few-shot prompted LLM inference. |
| Outcome: | The proposed algorithm achieves sizable accuracy boosts over state-of-the-art forward reasoning methods on two challenging logical reasoning datasets. |
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