arXiv:2512.23847v2 Announce Type: replace-cross Abstract: We develop a statistical procedure to detect lookahead bias in economic forecasts generated by large language models (LLMs). Using a date-only recall query for a firm-date pair, we estimate the probability that the LLM has internalized information about the realized outcome, a statistic we term Lookahead Propensity (LAP). LAP is materially positive throughout the in-sample period and collapses essentially to zero right after the training-data cutoff. We show that a positive interaction between LAP and the LLM forecast in an accuracy reg
The rapid deployment and increasing reliance on large language models for forecasting across various sectors, including finance, necessitate robust methods to detect and mitigate inherent biases.
Sophisticated readers in finance and technology should care because undiscovered lookahead bias in LLM forecasts can lead to systematically flawed decisions, financial losses, and eroded trust in AI-driven tools.
This research introduces a novel statistical procedure (LAP) for quantitatively assessing lookahead bias in LLM-generated economic forecasts, providing a critical tool for model validation and improved reliability.
- · AI model developers
- · Financial institutions (AI-adopters)
- · Regulatory bodies
- · LLM developers (unaware of bias)
- · Financial forecasters relying on unvalidated LLMs
- · Users making decisions based on biased LLM output
The LAP metric will become a standard for evaluating the integrity of LLM forecasts, particularly in high-stakes applications.
Increased scrutiny on LLM training data and methodologies will emerge, potentially leading to new best practices for data cutoff management and continuous learning in financial AI.
A competitive advantage will accrue to firms capable of demonstrating minimal lookahead bias in their AI models, accelerating the adoption of responsible AI in finance.
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Read at arXiv cs.LG