Unleashing the Infinite Complexity of Deductive Reasoning in Medical Diagnosis

The Significance of Deductive Reasoning in Artificial Intelligence

Deductive reasoning is essential in tasks like medical diagnosis and theorem proving. It allows for complex proofs to be built using numerous deduction rules and subproofs. To create a general reasoning model that can handle a wide range of reasoning tasks, a team of NYU and Google AI researchers has employed Large Language Models (LLMs) for deductive reasoning using in-context learning (ICL) and chain-of-thought (CoT) prompting.

Research Significance

Researchers aim to understand the ability of LLMs to generalize to proofs that are more sophisticated than their demonstrations. This is important because the size of a proof is determined by the number of premises, the length of the proof, and the rules of deduction. The group builds on previous research by testing if LLMs can generalize beyond modus ponens, the primary emphasis of earlier research. Their reasoning abilities are tested in two ways: depth- and width-generalization, and compositional generalization.

Research Insights

The research found that reasoning tasks benefit most from in-context learning when presented with examples that illustrate a variety of deduction rules. In-context examples should include principles the model is unfamiliar with, accompanied by distractors to prevent overfitting. CoT can induce reasoning in LLMs that generalize to compositional proofs.

Future Aid in Deductive Reasoning

Future research will explore how to further characterize extrapolation from specific instances. Researchers found that even though the best examples came from a different distribution, simpler examples worked better. This opens up new opportunities for reasoning tasks in AI and LLMs’ general deduction rules.

Conclusions

The use of LLMs for deductive reasoning tasks is promising and provides valuable insights into AI reasoning capabilities. It will lead to better understanding of how to improve reasoning abilities in AI models. If you’re interested in this topic, make sure to read the full paper on the topic here.┬áSign up to the latest AI research news, AI projects, and more.

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