Grounding Text Embeddings in Stakeholder Associations
Text embeddings are widely used to analyse large corpora of complex texts. However, it is unclear whether the embeddings capture the same semantic distances as the human experts using them. Ensuring alignment between embedding representations and human intentions is essential for valid analyses. We present the Stakeholder Grounding Exercise, a method for making expert associations explicit and grounding embedding model results in human understanding. In our primary case study on Danish policy issues, we find that neural text embeddings are substantially less reliable than human experts (19-26 pp gap), and that this misalignment propagates to downstream clustering performance (Spearman $ρ=...
| Authors | Jonathan Rystrøm, Sofie Burgos-Thorsen, Zihao Fu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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