# Faithful or Findable? Evaluating LLM-Generated Metadata for RDF Dataset Search

arXiv: https://arxiv.org/abs/2607.05970 · HTML: https://mlsanity.com/paper/2607.05970/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 7 Jul 2026.

## Builds on (most cited first, 2 of 2)

- [Dataset Discovery in Data Lakes](https://mlsanity.com/paper/2011.10427/) (2020)
- [AutoDDG: Automated Dataset Description Generation using Large Language Models](https://mlsanity.com/paper/2502.01050/) (2025)

## Abstract (arXiv)

Dataset search depends heavily on metadata, making LLM-generated metadata a consequential form of synthetic content in retrieval systems. We study six metadata-generation settings for RDF datasets, ranging from simple rewriting to profile-grounded and agentic graph-based generation, and evaluate them jointly for retrieval effectiveness and faithfulness. Unconstrained metadata rewriting delivers the strongest retrieval gains over the original metadata, but it is also the least faithful, showing that search improvements can be driven by unsupported semantic expansion. More grounded settings substantially improve faithfulness, and profile-grounded rewriting provides the most balanced trade-off between retrieval effectiveness and grounding. These findings position synthetic metadata as a system-level IR problem in which effectiveness, provenance, and trust must be evaluated together.