RESEARCH TOPIC / DATA-QUALITY-KNOWLEDGE
Data quality, freshness, stewardship, and decision use.
GROUNDING MAP
Explore 5 research notes through a shared decision domain. Each article identifies its conclusion, sources, findings, and limitations.
We examine the inclusion of the public test set in training data from multiple perspectives, including data duplication, completion behavior, and example order, to isolate suspicious scores from decision-making.
Read article
Instead of uniformly prohibiting the reuse of synthetic data, we design learning data operations that separate the replacement, accumulation, and selection of real data to preserve rare cases and diversity.
Read article
By tracking data quality not only in terms of accuracy but also meaning, proxy indicators, and on-site correction pathways, we identify where numbers lead to judgment errors.
Read article
RAG accuracy is determined not only by the model but also by how quickly document version, expiration, source, permissions, and deletion are reflected in the index; we consider operational design from knowledge benchmarks.
Read article
Data volume and decision quality are not the same. We examine the conditions for a usable data foundation, considering metric meaning, causality versus correlation, update frequency, and on-the-ground context.
Read article
