Programming with Data: Test-Driven Data Engineering for Self-Improving LLMs from Raw Corpora

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This episode discusses the challenges of transferring human knowledge into large language models and presents a structured approach to data engineering that parallels software development.

🤗 Upvotes: 75 | cs.SE, cs.AI Authors: Chenkai Pan, Xinglong Xu, Yuhang Xu, Yujun Wu, Siyuan Li, Jintao Chen, Conghui He, Jingxuan Wei, Cheng Tan Title: Programming with Data: Test-Driven Data Engineering for Self-Improving LLMs from Raw Corpora Arxiv: http://arxiv.org/abs/2604.24819v1 Abstract: 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 operative way: training data becomes source code specifying what the model should learn, model training becomes compilation, benchmarking becomes unit testing, and failure-driven data repair becomes…

People in this episode

Hosts: Jingwen Liang, Gengyu Wang

Topics covered

• data engineering
• large language models
• artificial intelligence
• test-driven development
• knowledge representation
• model training
• data repair

Keywords

• test-driven data engineering
• self-improving LLMs
• domain corpora
• model failures
• data deficiencies
• knowledge transfer
• feedback mechanisms

Mentioned in this episode

Organizations: arxiv

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