构建时间一致的指令微调模型，消除未来信息泄露问题。
Instruction Tuning Chronologically Consistent Language Models
- 基于截止日期前数据训练，确保时间顺序无泄露。
- 提供可复现的开放模型权重和对话式交互界面。
- 适合需要严格时间约束的预测研究，如历史分析与趋势推演。
我们提出一系列时间一致的指令微调大语言模型，以消除前瞻偏差。每个模型仅使用在明确知识截止日期前可用的数据进行训练，确保与截止日期后数据严格隔离。该框架具备（i）简洁的对话式聊天界面，（ii）完全开放且固定的模型权重，保证结果可复现，（iii）保守的预测准确率下界，剥离训练泄漏后仍能保留的可预测性部分。这些特性共同为研究人员提供了一种易用、无前瞻偏差的生成式AI工具，适用于广泛预测任务。
原文摘要 · Abstract (English)
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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