Abductive Reasoning with the GPT-4 Language Model: Case studies from criminal investigation, medical practice, scientific research

This study evaluates the GPT-4 Large Language Model's abductive reasoning in complex fields like medical diagnostics, criminology, and cosmology. Using an interactive interview format, the AI assistant demonstrated reliability in generating and selecting hypotheses. It inferred plausible medical diagnoses based on patient data and provided potential causes and explanations in criminology and cosmology. The results highlight the potential of LLMs in complex problem-solving and the need for further research to maximize their practical applications. Keywords: GPT-4 Language Model, Abductive Reasoning, Medical Diagnostics, Criminology, Cosmology, Hypothesis Generation 1 Introduction The rise of Large Language Models (LLMs) like GPT-4 (OpenAI, 2023) has marked a significant milestone in artificial intelligence, demonstrating an exceptional ability to mimic human-like text. Yet, this progress has sparked intense discussions among scholars. The discourse is largely polarized between two perspectives: one, the critique that these models, often referred to as "stochastic parrots" (Bender et al., 2021), are devoid of true creativity, and two, the counter-argument that they possess an excessive degree of inventiveness often yielding outputs that veer more towards the realm of fantasy than fact. This article investigates these debates, specifically within the context of abductive reasoning, a field that demands a careful balance between creativity and constraint. Abductive reasoning, often called "inference to the best explanation," involves generating and evaluating hypotheses to explain observations.