发表机构
Stevens Institute of Technology（史蒂文斯理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究用GPT-3.5模型开展在线借贷反事实分析，经提示工程优化后其预测性能接近梯度提升回归，验证了LLMs用于反事实分析的潜力。
AI 中文摘要
反事实分析旨在预测假设场景下的潜在结果，为决策提供有价值的见解。本文研究大语言模型（LLMs）在反事实分析中的应用，具体采用GPT-3.5模型，聚焦在线借贷场景，该场景下的反事实投资回报率（ROI）对评估不同利率方案至关重要。首先评估GPT的预测性能，并与高级机器学习算法对比，结果显示提示工程可显著提升GPT的预测效果，决定系数（R-squared）从1.97%提升至2.84%，接近梯度提升回归达到的3.48%。随后，利用GPT生成一系列替代利率下的反事实ROI，其响应表现出逻辑连贯性与因果推理能力。研究结果强调LLMs作为在线借贷反事实分析有效工具的潜力，表明LLMs在各类预测与决策场景中具有更广泛的应用前景。
英文摘要
Counterfactual analysis aims to predict potential outcomes under hypothetical scenarios, offering valuable insights for decision-making. This paper investigates the application of large language models (LLMs), specifically the GPT-3.5 model, for counterfactual analysis. We focus on the online lending context, where the counterfactual return on investment (ROI) is crucial for evaluating different interest rate schemes. We begin by assessing the predictive performance of GPT and comparing it with advanced machine learning algorithms. The results show that prompt engineering can significantly enhance GPT's predictions, with the R-squared increasing from 1.97% to 2.84%, closely approaching the 3.48% achieved by gradient-boosted regression. Subsequently, we utilize GPT to generate counterfactual ROIs under a set of alternative interest rates. GPT exhibits logical coherence and causal reasoning in its responses. The findings underscore the potential of LLMs as effective tools for counterfactual analysis in online lending, suggesting broader applications for LLMs in various predictive and decision-making contexts.
