NoLBERT: A No Lookahead(back) Foundational Language Model

Ali Kakhbod and Peiyao Li

Papers from arXiv.org

Abstract: We present NoLBERT, a lightweight, timestamped foundational language model for empirical research -- particularly for forecasting in economics, finance, and the social sciences. By pretraining exclusively on text from 1976 to 1995, NoLBERT avoids both lookback and lookahead biases (information leakage) that can undermine econometric inference. It exceeds domain-specific baselines on NLP benchmarks while maintaining temporal consistency. Applied to patent texts, NoLBERT enables the construction of firm-level innovation networks and shows that gains in innovation centrality predict higher long-run profit growth.

Date: 2025-09, Revised 2025-11
New Economics Papers: this item is included in nep-net
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Published in NeurIPS 2025 (GenAI in Finance)

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