Chronologically Consistent Generative AI

2025-10-13

AI Summary

This paper addresses *prospective bias* in large language models (LLMs) for forecasting tasks—arising when training data inadvertently includes future information. To eliminate this bias, we propose a *temporal consistency framework* that strictly restricts training data to sources predating a predefined knowledge cutoff date. Leveraging temporal isolation and instruction tuning, the framework constructs a family of fixed-weight, time-aware instruction-following models, thereby preventing data leakage during training. Our key contribution is the first formal definition and implementation of a *prospective-bias-free generative forecasting paradigm*, yielding reproducible knowledge boundaries and conservative yet reliable prediction lower bounds. Experiments demonstrate substantial improvements in forecast credibility and reproducibility. The framework establishes a rigorous methodological foundation for LLM-based temporal inference, policy simulation, and societal forecasting, accompanied by open-source tooling.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

We introduce a family of chronologically consistent, instruction-following 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.

Problem

• Eliminating lookahead bias in AI models
• Training models only on pre-cutoff historical data
• Providing temporally consistent prediction tools

Innovation

• Chronologically consistent models eliminate lookahead bias
• Training uses only data before a cutoff date
• Open model weights ensure replicability and predictability