A Test of Lookahead Bias in LLM Forecasts
Published:Dec 29, 2025 20:20
•1 min read
•ArXiv
Analysis
This paper introduces a novel statistical test, Lookahead Propensity (LAP), to detect lookahead bias in forecasts generated by Large Language Models (LLMs). This is significant because lookahead bias, where the model has access to future information during training, can lead to inflated accuracy and unreliable predictions. The paper's contribution lies in providing a cost-effective diagnostic tool to assess the validity of LLM-generated forecasts, particularly in economic contexts. The methodology of using pre-training data detection techniques to estimate the likelihood of a prompt appearing in the training data is innovative and allows for a quantitative measure of potential bias. The application to stock returns and capital expenditures provides concrete examples of the test's utility.
Key Takeaways
- •Introduces Lookahead Propensity (LAP) as a metric to quantify lookahead bias.
- •Provides a statistical test to detect lookahead bias in LLM forecasts.
- •Offers a cost-efficient diagnostic tool for assessing the reliability of LLM-generated forecasts.
- •Applies the test to news headlines predicting stock returns and earnings call transcripts predicting capital expenditures.
Reference
“A positive correlation between LAP and forecast accuracy indicates the presence and magnitude of lookahead bias.”