Instruction Tuning Chronologically Consistent Language Models

Songrun He; Linying Lv; Asaf Manela; Jimmy Wu

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arXiv · 2510.11677

Instruction Tuning Chronologically Consistent Language Models

Songrun He·Linying Lv·Asaf Manela·Jimmy Wu

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Abstract

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.

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Songrun He, Linying Lv, Asaf Manela, Jimmy Wu. 2025-10-13. Instruction Tuning Chronologically Consistent Language Models. https://arxiv.org/abs/2510.11677

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