First Survey of Abductive Reasoning in LLMs Traces Philosophical Roots to AI

A recent study published on arXiv offers the first thorough examination of abductive reasoning within Large Language Models (LLMs), charting its evolution from philosophical roots to modern AI applications. This research highlights the often-overlooked aspect of abductive reasoning—drawing the most likely explanation from observations—despite its crucial importance in human understanding and discovery. The authors propose a cohesive two-stage framework that categorizes earlier research, separating abduction into Hypothesis Generation, where models identify potential explanations, and Hypothesis Selection, where these options are assessed to determine the most credible one. This structure aims to clarify prevalent conceptual ambiguities and inconsistent task definitions in the discipline. The paper is intended as a foundational reference for future explorations of abductive reasoning in LLMs.