发现大模型投资推荐存在系统性产品偏见，可能引发市场风险。
Exposing Product Bias in LLM Investment Recommendation
- 构建56.7万样本数据集，跨五类资产研究大模型推荐偏好
- 大模型显著偏好苹果、微软等特定股票，且去偏后仍存在
- 提醒研究人员关注算法偏见对金融市场的潜在危害
大型语言模型（LLMs）作为新一代推荐引擎，具备强大的摘要与数据分析能力，在范围和性能上超越传统推荐系统。一个有前景的应用是投资推荐。本文揭示了大模型投资推荐中的一种新型产品偏见，即大模型对特定产品存在系统性偏好。这种偏好可能微妙影响用户投资决策，导致产品估值虚高，甚至引发金融泡沫，威胁个人投资者与市场稳定。为全面研究该偏见，我们开发了一个自动化管道，创建了包含567,000个样本的数据集，覆盖股票、共同基金、加密货币、储蓄及投资组合五类资产。这是首次系统研究大模型在投资推荐中的产品偏见。研究发现，大模型明显偏好特定股票（如`AAPL'来自苹果公司、`MSFT'来自微软公司）。值得注意的是，即使应用了去偏技术，这种偏见依然持续存在。我们呼吁人工智能研究人员重视大模型投资推荐中的产品偏见及其影响，保障数字空间与市场的公平与安全。
原文摘要 · Abstract (English)
Large language models (LLMs), as a new generation of recommendation engines, possess powerful summarization and data analysis capabilities, surpassing traditional recommendation systems in both scope and performance. One promising application is investment recommendation. In this paper, we reveal a novel product bias in LLM investment recommendation, where LLMs exhibit systematic preferences for specific products. Such preferences can subtly influence user investment decisions, potentially leading to inflated valuations of products and financial bubbles, posing risks to both individual investors and market stability. To comprehensively study the product bias, we develop an automated pipeline to create a dataset of 567,000 samples across five asset classes (stocks, mutual funds, cryptocurrencies, savings, and portfolios). With this dataset, we present the bf first study on product bias in LLM investment recommendations. Our findings reveal that LLMs exhibit clear product preferences, such as certain stocks (e.g., `AAPL' from Apple and `MSFT' from Microsoft). Notably, this bias persists even after applying debiasing techniques. We urge AI researchers to take heed of the product bias in LLM investment recommendations and its implications, ensuring fairness and security in the digital space and market.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。