The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The paper introduces DiaWhisper-DPO, an end‑to‑end model that fine‑tunes Whisper-large-v3 with LoRA and a frame‑level role head to transcribe and attribute utterances in clinical interviews. It further refines the system using failure‑mined preference optimization (DPO) that leverages genuine decoding failures as rejected completions, eliminating the need for human preference data. On the DAIC‑WOZ dataset, DiaWhisper‑DPO attains 0.973 role accuracy and 0.119 DER, outperforming cascaded baselines by 72% and dramatically reducing seed variation, while also improving performance on the cross‑lingual PDCH‑HAMD dataset.
arXiv:2606. 06902v1 Announce Type: new Abstract: Targeted post-training aims to improve reasoning, math, and code without degrading strengths.
arXiv:2605.22286v2 Announce Type: replace-cross Abstract: Text-based counseling provides a valuable source of information for assessing depression severity. We study prediction of the total score on...
Automated depression screening from clinical interviews requires attribution of utterances to the clinician or patient. We evaluate two datasets: DAIC-WOZ, where participant-only recordings require re...
LongCounsel-8 is a new benchmark suite comprising three datasets with 7,749 five‑session counseling dialogues, each grounded in real client profiles, depression trajectories, symptom compositions, and counseling patterns. The benchmark addresses challenges of longitudinal consistency, empirical grounding of symptom progression, and natural expression of controlled depression states without exposing labels. Experiments show that lower single‑session error does not ensure accurate trend detection, methods perform worse on worsening trajectories, and adding more session history can reduce trend prediction accuracy.
arXiv:2606.28798v2 Announce Type: replace Abstract: The main objective of this paper is to propose a general framework for prediction based on different sources of multimodal data in the healthcare d...