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Instruction Tuning Chronologically Consistent Language Models

Author

Listed:
  • Songrun He
  • Linying Lv
  • Asaf Manela
  • Jimmy Wu

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.

Suggested Citation

  • Songrun He & Linying Lv & Asaf Manela & Jimmy Wu, 2025. "Instruction Tuning Chronologically Consistent Language Models," Papers 2510.11677, arXiv.org, revised Nov 2025.
    • Handle: RePEc:arx:papers:2510.11677
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    References listed on IDEAS

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    1. Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2024. "Large Language Models: An Applied Econometric Framework," Papers 2412.07031, arXiv.org, revised Dec 2025.
    2. Manish Jha & Jialin Qian & Michael Weber & Baozhong Yang, 2024. "ChatGPT and Corporate Policies," Papers 2409.17933, arXiv.org, revised Feb 2025.
    3. Paul Glasserman & Caden Lin, 2023. "Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis," Papers 2309.17322, arXiv.org.
    4. Songrun He & Linying Lv & Asaf Manela & Jimmy Wu, 2025. "Chronologically Consistent Large Language Models," Papers 2502.21206, arXiv.org, revised Jul 2025.
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