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...
The study investigates how a large language model, Gemma-3-27B-PT, internally represents depressive symptoms. By applying mechanistic interpretability methods to the model’s residual stream, researchers found that symptom groups are geometrically distinct at layer 21, and that projected symptom vectors align with clinician-annotated rankings across mood, somatic, and suicidality dimensions. Additionally, a single depression vector at this layer can differentiate depressive from non-depressive text with an AUC of 0.789, suggesting a potential emotional valence gate for symptom projection.
arXiv:2511.07011v2 Announce Type: replace-cross Abstract: Background: Remotely captured spoken language could provide objective, regular indicators of depression symptom severity. However, research t...
arXiv:2502. 09487v4 Announce Type: replace-cross Abstract: Narratives and emotions shape thoughts, and thoughts shape our feelings and stories we tell.
The paper investigates Mandarin Chinese reduplicative constructions that repeat two-character base words or their constituents, such as expressions meaning ‘in good health’ or ‘discuss a bit’. Using Tencent word embeddings, the study demonstrates that distributional semantics can recover known semantic and grammatical properties of these reduplications, revealing clear semantic and pragmatic differentiation between the two patterns. Procrustes analysis shows that the overall organization of the base-word space is largely preserved in the reduplication space, with local mismatches indicating discourse-pragmatic reorganization.
The paper introduces a two‑stage framework for recognizing depression symptoms at the sentence level. First, a contrastively fine‑tuned sentence encoder generates a symptom candidate for each sentence. Then, a fine‑tuned language model verifies the candidate’s presence or absence by comparing the sentence, its context, and a diagnostic definition, ensuring the model’s judgment aligns with that definition before responding.
arXiv:2602. 15851v2 Announce Type: replace-cross Abstract: Applications of narrative theories using large language models (LLMs) deliver promising methods in automatic story generation and understanding tasks.
BiGraph-Diffuse is a large‑scale diffusion language model designed for mental health counseling, addressing two key limitations of existing AI dialogue systems: the lack of bidirectional understanding for progressive disclosure and the inadequate use of relational clinical knowledge. It pairs this diffusion model with BiGraph‑RAG, a graph‑structured retrieval approach that uses lightweight entity extraction and semantic linking to preserve inferential pathways from symptoms to underlying causes without incurring LLM token costs during indexing. Experiments and theoretical analysis demonstrate the effectiveness of this mutually reinforcing architecture.
arXiv:2606. 28798v1 Announce Type: new Abstract: Objective: ICD codes are central to reimbursement, research, and population health surveillance, yet automated coding systems often struggle to integrate diagnostic signals from both clinical narratives and structured electronic health record (EHR) variables.
The paper introduces a structured framework for claim-level discourse analysis in dense health narratives, addressing the limitations of existing topic- or sentiment-based representations. It identifies an average of 13.22 atomic claims per minute in social media health videos and proposes tuples that link each claim to thematic aspects, stance, and multidimensional pragmatic attributes. A benchmark of 1,191 manually annotated claims from 60 videos across four health domains is created, and experiments show that large language models perform well on thematic categorization and stance prediction but struggle with high-dimensional pragmatic profiling, indicating a need for task-specific inference strategies.
arXiv:2512. 04124v4 Announce Type: replace-cross Abstract: Frontier language models increasingly participate in conversations about distress and mental health, yet the mechanisms that generate anthropomorphic self narratives remain unclear.
arXiv:2609.36218v1 Announce Type: cross Abstract: Large language models are increasingly evaluated in specialized domains such as law, medicine, software engineering, and cybersecurity, yet film rema...