Researchers Develop Test Detecting Lookahead Bias in Forecasts
Researchers present a statistical test to detect lookahead bias in economic forecasts generated by large language models in a paper submitted Dec. 29, 2025. They estimate a Lookahead Propensity (LAP) score using pre-training data detection techniques and show that a positive correlation between LAP and forecast accuracy quantifies lookahead bias. The test is applied to headline-based stock-return and earnings-call capex forecasts and offered as a cost-efficient diagnostic tool.
Key Points
- 1Introduce Lookahead Propensity (LAP) metric estimating prompt presence in LLM training data
- 2Demonstrate positive LAP–accuracy correlation signals lookahead bias and measures its magnitude
- 3Provide cost‑efficient diagnostic test applicable to headline and earnings‑call forecasting pipelines
Scoring Rationale
Novel and actionable diagnostic scoring high on relevance, limited by single preprint evaluation and narrower economic forecasting scope.
Sources
Public references used for this report.
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