Multi-Objective Aligned Small Language Model Framework for SUD Patient Dialogue Generation

Key points

• Substance Use Disorder (SUD) counseling requires patient responses that reflect underlying cognitive states such as beliefs, coping strategies, and readiness for change.1
• 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).1
• Our pipeline consists of two stages: cognitive component detection and cognitive component-aligned dialogue generation.1
• 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.1

Sources (1)

• [1]Multi-Objective Aligned Small Language Model Framework for SUD Patient Dialogue Generation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 11:10 PM

  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.

  Substance Use Disorder (SUD) counseling requires patient responses that reflect underlying cognitive states such as beliefs, coping strategies, and readiness for change.

Extractive summary: sentences quoted from the sources.