Behind the [MASK]: Disentangling Representation and Faithfulness in DAPF-Based Dementia Detection
arXiv:2608.25028v1 Announce Type: new Abstract: Spoken-language analysis via prompt-based domain-adaptive models is a promising direction for low-resource, non-invasive dementia screening, but such models remain internally opaque. We study the interpretability of the Domain-Adapted models via Prompt-based Fine-tuning (DAPF) framework, which casts dementia detection as diagnosis-related masked-token prediction. We interpret DAPF and strong baselines using a variety of probing and analysis techniques, finding that DAPF achieved the best overall performance (accuracy=0.83 and macro-F1=0.83) with diagnosis most recoverable from its [MASK] representation. However, this representational advantage did not extend to token-level explanation faithfulness. DAPF attributions primarily reflected language task vocabulary, discourse markers, and transcription artifacts, with perturbation tests showing weak or negative effects. This suggests that its masked-token interface determines diagnosis information without producing faithful token-level explanations.
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[Submitted on 25 Aug 2026]
Title:Behind the [MASK]: Disentangling Representation and Faithfulness in DAPF-Based Dementia Detection
View a PDF of the paper titled Behind the [MASK]: Disentangling Representation and Faithfulness in DAPF-Based Dementia Detection, by Pardis Ranjbar-Noiey and 1 other authors
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Abstract:Spoken-language analysis via prompt-based domain-adaptive models is a promising direction for low-resource, non-invasive dementia screening, but such models remain internally opaque. We study the interpretability of the Domain-Adapted models via Prompt-based Fine-tuning (DAPF) framework, which casts dementia detection as diagnosis-related masked-token prediction. We interpret DAPF and strong baselines using a variety of probing and analysis techniques, finding that DAPF achieved the best overall performance (accuracy=0.83 and macro-F1=0.83) with diagnosis most recoverable from its [MASK] representation. However, this representational advantage did not extend to token-level explanation faithfulness. DAPF attributions primarily reflected language task vocabulary, discourse markers, and transcription artifacts, with perturbation tests showing weak or negative effects. This suggests that its masked-token interface determines diagnosis information without producing faithful token-level explanations.
Comments: 16 pages, 1 figure, 19 tables. Under review at ACL Rolling Review
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2608.25028 [cs.CL]
(or arXiv:2608.25028v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.25028
arXiv-issued DOI via DataCite (pending registration)
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From: Pardis Ranjbar-Noiey [view email] [v1] Tue, 25 Aug 2026 18:16:05 UTC (66 KB)
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