DatedGPT: Preventing Lookahead Bias in Large Language Models with Time-Aware Pretraining
arXiv:2603.11838v1 Announce Type: new Abstract: In financial backtesting, large language models pretrained on internet-scale data risk introducing lookahead bias that undermines their forecasting validity, as they may have already seen the true outcome during training. To address this, we present DatedGPT, a family of twelve 1.3B-parameter language models, each trained from scratch on approximately 100 billion tokens of temporally partitioned data with strict annual cutoffs spanning 2013 to 2024. We further enhance each model with instruction fine-tuning on both general-domain and finance-specific datasets curated to respect the same temporal boundaries. Perplexity-based probing confirms that each model's knowledge is effectively bounded by its data cutoff year, while evaluation on standard benchmarks shows competitive performance with existing models of similar scale. We provide an interactive web demo that allows users to query and compare responses from models across different cuto
arXiv:2603.11838v1 Announce Type: new Abstract: In financial backtesting, large language models pretrained on internet-scale data risk introducing lookahead bias that undermines their forecasting validity, as they may have already seen the true outcome during training. To address this, we present DatedGPT, a family of twelve 1.3B-parameter language models, each trained from scratch on approximately 100 billion tokens of temporally partitioned data with strict annual cutoffs spanning 2013 to 2024. We further enhance each model with instruction fine-tuning on both general-domain and finance-specific datasets curated to respect the same temporal boundaries. Perplexity-based probing confirms that each model's knowledge is effectively bounded by its data cutoff year, while evaluation on standard benchmarks shows competitive performance with existing models of similar scale. We provide an interactive web demo that allows users to query and compare responses from models across different cutoff years.
Executive Summary
This study presents DatedGPT, a novel approach to mitigating lookahead bias in large language models by training them on temporally partitioned data with strict annual cutoffs. The authors develop a family of twelve 1.3B-parameter language models, each trained on approximately 100 billion tokens of data spanning 2013 to 2024. The results demonstrate that DatedGPT effectively bounds knowledge by its data cutoff year, while maintaining competitive performance on standard benchmarks. The study concludes with an interactive web demo for querying and comparing responses from models across different cutoff years. The study's findings and methodology have significant implications for the development and deployment of large language models in finance and other domains.
Key Points
- ▸ DatedGPT is a novel approach to mitigating lookahead bias in large language models.
- ▸ The models are trained on temporally partitioned data with strict annual cutoffs.
- ▸ The results demonstrate effective knowledge bounding by the data cutoff year.
Merits
Improved Generalizability
DatedGPT's knowledge bounding approach enhances the generalizability of large language models by preventing lookahead bias and ensuring that models are trained on data that is temporally relevant to the task at hand.
Competitive Performance
The study demonstrates that DatedGPT models maintain competitive performance on standard benchmarks, despite being trained on temporally partitioned data.
Demerits
Scalability Limitations
The study's approach may be limited by the scalability of training large language models on temporally partitioned data, which can be computationally expensive and may require significant resources.
Data Quality Concerns
The study's reliance on internet-scale data may raise concerns about data quality, particularly in terms of temporal relevance and accuracy.
Expert Commentary
The study presents a novel and timely approach to mitigating lookahead bias in large language models. While the results are promising, the scalability limitations and data quality concerns highlight the need for further research and development. The study's implications for explainability, transparency, and fairness in AI are significant, and its findings have far-reaching consequences for the development and deployment of large language models. As the field continues to evolve, it is essential to prioritize research that addresses these critical issues.
Recommendations
- ✓ Future studies should investigate the scalability limitations of DatedGPT and explore ways to mitigate them.
- ✓ Researchers should prioritize the development of methods for ensuring data quality and temporal relevance in large language models.
