Chronologically Consistent Large Language Models

Paper: arXiv 2502.21206

Abstract

Large language models are increasingly used in social sciences, but their training data can introduce lookahead bias and training leakage. A good chronologically consistent language model requires efficient use of training data to maintain accuracy despite time-restricted data. Here, we overcome this challenge by training a suite of chronologically consistent large language models, ChronoBERT and ChronoGPT, which incorporate only the text data that would have been available at each point in time. Despite this strict temporal constraint, our models achieve strong performance on natural language processing benchmarks, outperforming or matching widely used models (e.g., BERT), and remain competitive with larger open-weight models. Lookahead bias is model and application-specific because even if a chronologically consistent language model has poorer language comprehension, a regression or prediction model applied on top of the language model can compensate. In an asset pricing application predicting next-day stock returns from financial news, we find that ChronoBERT and ChronoGPT’s real-time outputs achieve Sharpe ratios comparable to a much larger Llama model, indicating that lookahead bias is modest. Our results demonstrate a scalable, practical framework to mitigate training leakage, ensuring more credible backtests and predictions across finance and other social science domains.

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs not modelled; see penalty flags)
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper employs sophisticated deep learning architectures (BERT, GPT) and complex training regimes, but the math is largely applied from established NLP literature rather than introducing novel theory. Empirical rigor is high, featuring extensive backtesting on financial news data, Sharpe ratio analysis, and comparisons to baselines, demonstrating a data-heavy, implementation-focused study.

Research Flowchart

  flowchart TD
  A["Research Goal: <br>Mitigate Lookahead Bias in LLMs <br>for Social Science"] --> B["Methodology: <br>Chronological Training"]
  B --> C{"Data Inputs: <br>Time-Restrained Text Data"}
  C --> D["Computational Process: <br>Train ChronoBERT & ChronoGPT"]
  D --> E["Outcomes 1: <br>NLP Benchmark Performance <br>(Match/Met BERT)"]
  D --> F["Outcomes 2: <br>Asset Pricing Application <br>(Stock Returns Prediction)"]
  F --> G["Result: <br>Comparable Sharpe Ratios <br>(Modest Lookahead Bias)"]

Cited References (85)

1. Lookahead Bias in Pretrained Language Models (2024) — link
2. Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis — → 2309.17322
3. Documenting Large Webtext Corpora : A Case Study on the Colossal Clean Crawled Corpus (2021) — link
4. StoriesLM : A Family of Language Models With Time-Indexed Training Data (2024)
5. American Stories : A Large - Scale Structured Text Dataset of Historical U . S . Newspapers (2023) — link
6. Improving Language Understanding by Generative Pre - Training (2018)
7. RoBERTa : A Robustly Optimized BERT Pretraining Approach (2019) — link
8. Language Models are Few - Shot Learners (2020) — link

2 of 85 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.