Скачать или смотреть session 28 : IDEA: Enhancing Rule Learning Through Inductive, Deductive, and Abductive Reasoning

IDEA: Enhancing Rule Learning Through Inductive, Deductive, and Abductive Reasoning

In this session, Shaygan Adim presents the IDEA framework for improving rule learning capabilities in large language models. The research explores how LLM agents can better learn and apply rules through three types of logical reasoning: inductive reasoning (learning patterns from examples), deductive reasoning (applying known rules to specific cases), and abductive reasoning (inferring the best explanation for observations). This comprehensive approach enhances the models’ ability to understand, internalize, and effectively utilize complex rule systems across various domains.

The IDEA framework represents a significant advancement in making LLMs more systematic and reliable in rule-based reasoning tasks.


1.IDEA: Enhancing the Rule Learning Ability of Large Language Model Agent through Induction, Deduction, and Abduction : https://arxiv.org/abs/2408.10455

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#LLM #RuleLearning #LogicalReasoning #InductiveReasoning #DeductiveReasoning #AbductiveReasoning #LanguageModels #ArtificialIntelligence #IDEA #LLMAgents