Instruction Tuning Chronologically Consistent Language Models
This addresses the issue of training leakage for researchers in prediction tasks, though it is incremental as it applies existing methods to new temporal constraints.
The authors tackled the problem of lookahead bias in language models by training instruction-tuned models only on data before specific cutoff dates, resulting in a framework that provides a conservative lower bound on forecast accuracy and ensures replicability.
We introduce a family of chronologically consistent, instruction-tuned large language models to eliminate lookahead bias. Each model is trained only on data available before a clearly defined knowledge-cutoff date, ensuring strict temporal separation from any post-cutoff data. The resulting framework offers (i) a simple, conversational chat interface, (ii) fully open, fixed model weights that guarantee replicability, and (iii) a conservative lower bound on forecast accuracy, isolating the share of predictability that survives once training leakage is removed. Together, these features provide researchers with an easy-to-use generative AI tool useful for a wide range of prediction tasks that is free of lookahead bias.