Programming with Data: Test-Driven Data Engineering for Self-Improving LLMs from Raw Corpora
Reliably transferring specialized human knowledge from text into large language models remains a fundamental challenge in artificial intelligence. Fine-tuning on domain corpora has enabled substantial capability gains, but the process operates without feedback: when a model fails on a domain task, there is no method to diagnose what is deficient in the training data, and the only recourse is to add more data indiscriminately. Here we show that when a structured knowledge representation extracted from the source corpus serves as the shared foundation for both training data and evaluation, the complete data-engineering lifecycle maps onto the software development lifecycle in a precise and ...
| Authors | Chenkai Pan, Xinglong Xu, Yuhang Xu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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