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arXiv Machine Learning By Pardis Ranjbar-Noiey, Natalie Parde

Behind the [MASK]: Disentangling Representation and Faithfulness in DAPF-Based Dementia Detection

Read the original on arXiv Machine Learning →

The paper investigates the interpretability of Domain-Adapted Prompt-based Fine-tuning (DAPF) models for dementia detection from spoken language. Using probing and analysis techniques, the authors find that DAPF achieves strong overall performance (accuracy = 0.83, macro‑F1 = 0.83) and that the diagnosis is most recoverable from its [MASK] representation. However, token‑level explanations derived from DAPF are largely driven by language‑task vocabulary, discourse markers, and transcription artifacts, and perturbation tests reveal weak or negative effects, indicating that the masked‑token interface captures diagnosis information without providing faithful token‑level explanations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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