Computer Science > Artificial Intelligence
[Submitted on 20 Dec 2022 (v1), last revised 29 May 2023 (this version, v2)]
Title:LAMBADA: Backward Chaining for Automated Reasoning in Natural Language
View PDFAbstract:Remarkable progress has been made on automated reasoning with natural text, by using Language Models (LMs) and methods such as Chain-of-Thought and Selection-Inference. These techniques search for proofs in the forward direction from axioms to the conclusion, which suffers from a combinatorial explosion of the search space, and thus high failure rates for problems requiring longer chains of reasoning. The classical automated reasoning literature has shown that reasoning in the backward direction (i.e. from the intended conclusion to supporting axioms) is significantly more efficient at proof-finding. Importing this intuition into the LM setting, we develop a Backward Chaining algorithm, called LAMBADA, that decomposes reasoning into four sub-modules. These sub-modules are simply implemented by few-shot prompted LM inference. We show that LAMBADA achieves sizable accuracy boosts over state-of-the-art forward reasoning methods on challenging logical reasoning datasets, particularly when deep and accurate proof chains are required.
Submission history
From: Seyed Mehran Kazemi [view email][v1] Tue, 20 Dec 2022 18:06:03 UTC (289 KB)
[v2] Mon, 29 May 2023 16:23:29 UTC (306 KB)
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