Multi-Objective Aligned Small Language Model Framework for SUD Patient Dialogue Generation
Quick Answer
This paper shows that A new framework for generating dialogues in Substance Use Disorder (SUD) counseling uses small language models (SLMs) aligned with cognitive components.
Quick Take
This approach improves the realism and coherence of patient responses while addressing challenges like computational cost and privacy in healthcare settings. Evaluations show significant enhancements in cognitive alignment over generic models, particularly for open-ended components.
Key Points
- Proposes a two-stage pipeline: cognitive detection and aligned dialogue generation.
- Combines knowledge distillation, preference optimization, and attention-guided reward shaping.
- Achieves improved scores on BERTScore, ROUGE, METEOR, and BLEU metrics.
- Demonstrates strong performance gains for open-ended cognitive components.
- Addresses ethical and practical challenges in deploying in healthcare.
DeepSignal Analysis
What happened
A new framework for generating dialogues in Substance Use Disorder (SUD) counseling has been proposed, utilizing small language models (SLMs) that align with cognitive components. This framework aims to enhance the realism and coherence of patient responses while addressing issues like computational cost and privacy in healthcare settings.
Key evidence
- The proposed framework includes two stages: cognitive component detection and cognitive component-aligned dialogue generation.
- Evaluations indicated that fine-tuning with cognitive information significantly improved cognitive alignment compared to generic instruction-tuned models.
- The framework employs techniques such as knowledge distillation from larger models and preference optimization from human annotations.
Why it matters
This framework is significant as it addresses the limitations of large language models in producing clinically realistic patient dialogues, particularly in resource-constrained healthcare environments. By focusing on cognitive alignment, it aims to improve therapeutic interactions, which could lead to better patient outcomes in SUD counseling.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract:Substance Use Disorder (SUD) counseling requires patient responses that reflect underlying cognitive states such as beliefs, coping strategies, and readiness for change. Although large language models (LLMs) can generate fluent text, they often fail to produce cognitively coherent and clinically realistic patient behavior, especially under ethical and data-scarce clinical settings. Moreover, deploying frontier-scale LLMs in healthcare applications presents practical challenges including high computational cost, latency, privacy concerns, and limited deployability in resource-constrained environments, motivating the need for cognitively aligned small language models (SLMs). We propose a cognitively grounded framework for SUD patient dialogue generation that explicitly models and aligns latent cognitive components with patient histories and counselor questions. Our pipeline consists of two stages: cognitive component detection and cognitive component-aligned dialogue generation. To enable effective learning with smaller models, we combine knowledge distillation from high-capacity teacher models, preference optimization from human-annotations, and attention-guided reward shaping. Extensive evaluations using automatic scores like BERTScore, ROUGE, METEOR and BLEU, and LLM-as-judge hit-metrics against both human and teacher-model references show that cognitively informed fine-tuning substantially improves cognitive realization and alignment over a generic instruction-tuned baselines and mental health domain specific SLMs, with particularly strong gains for open-ended cognitive components.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.09209 [cs.CL] |
| (or arXiv:2610.09209v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09209 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Thushara Manjari Naduvilakandy [view email]
[v1]
Tue, 6 Oct 2026 23:10:21 UTC (3,813 KB)
— Originally published at arxiv.org
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