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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.11677 |
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| _version_ | 1866914160844996608 |
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| author | He, Songrun Lv, Linying Manela, Asaf Wu, Jimmy |
| author_facet | He, Songrun Lv, Linying Manela, Asaf Wu, Jimmy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11677 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Instruction Tuning Chronologically Consistent Language Models He, Songrun Lv, Linying Manela, Asaf Wu, Jimmy Machine Learning General Finance 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. |
| title | Instruction Tuning Chronologically Consistent Language Models |
| topic | Machine Learning General Finance |
| url | https://arxiv.org/abs/2510.11677 |