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.

本研究评估了 GPT-4 大型语言模型在医学诊断、犯罪学和宇宙学等复杂领域中的推理能力。通过交互式面试形式，该 AI 助手展示了在生成和选择假设方面的可靠性。它基于病人数据推测出可行的医学诊断，并在犯罪学和宇宙学领域提供了潜在的原因和解释。研究结果突出了 LLM 在复杂问题解决方面的潜力，并强调了进一步研究以最大化它们实际应用的必要性。

GPT-4 语言模型的诱因推理：刑侦、医疗实践和科学研究的案例研究

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

Scoring systems are classification models that only require users to add,
subtract and multiply a few meaningful numbers to make a prediction. These
models are often used because they are practical and interpretable. In this
paper, we introduce an off-the-shelf tool to create scoring systems that both
accurate and interpretable, known as a Supersparse Linear Integer Model (SLIM).
SLIM is a discrete optimization problem that minimizes the 0-1 loss to
encourage a high level of accuracy, regularizes the L0-norm to encourage a high
level of sparsity, and constrains coefficients to a set of interpretable
values. We illustrate the practical and interpretable nature of SLIM scoring
systems through applications in medicine and criminology, and show that they
are are accurate and sparse in comparison to state-of-the-art classification
models using numerical experiments.

本文介绍了一种名为 Supersparse Linear Integer Model 的分类模型，可以用于创建精准、可解释的评分系统，主要应用于医学和犯罪学领域，并通过数值试验表明其较之基于数值的分类模型具有更高的精度和稀疏度。