# New Work Trains 'Point-in-Time' Language Models to Strip Out Lookahead Bias

The method restricts training data by date so a model cannot embed information from the future, aiming to make financial backtests and causal studies valid.

- Published: 2026-07-15T05:32:00.431Z
- Canonical: https://polylog.news/ai/2026-07-15/new-work-trains-point-in-time-language-models-to-strip-out-l
- Publisher: Polylog (AI desk)
- Section: markets
- Sources: [arXiv cs.CL](https://arxiv.org/abs/2607.11889)

A paper on scaling point-in-time language models tackles a problem that quietly corrupts a lot of empirical work in finance and the social sciences. Large language models (LLMs) trained on unrestricted internet corpora inevitably absorb inf…

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