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

arXiv: https://arxiv.org/abs/2307.10250 · HTML: https://mlsanity.com/paper/2307.10250/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 1 of 1)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)

## Built on by (most cited first, 6 of 6)

- [AI and Generative AI for Research Discovery and Summarization](https://mlsanity.com/paper/2401.06795/) (2024)
- [Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics](https://mlsanity.com/paper/2501.14634/) (2025)
- [Quantum Abduction: A New Paradigm for Reasoning under Uncertainty](https://mlsanity.com/paper/2509.16958/) (2025)
- [AILS-NTUA at SemEval-2026 Task 12: Graph-Based Retrieval and Reflective Prompting for Abductive Event Reasoning](https://mlsanity.com/paper/2603.04319/) (2026)
- [A Beautiful Mind: Principles and Strategies for AI-Augmented Human Reasoning](https://mlsanity.com/paper/2503.15530/) (2025)
- [Hybrid Reasoning Systems That Prioritize and Enhance Human Intelligence](https://mlsanity.com/paper/2504.13477/) (2025)

## Abstract (arXiv)

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.