"Fine Tuning Large Language Models to Deliver CBT for Depression"

This paper demonstrates how fine-tuning small 7-8B parameter LLMs can effectively deliver Cognitive Behavioral Therapy (CBT) for depression, achieving significant improvements in therapeutic competencies compared to base models.

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https://arxiv.org/abs/2412.00251

🤖 Original Problem:

Depression affects 20.6% of Americans, but many can't access CBT therapy due to cost, stigma, and therapist scarcity. AI could help, but current solutions using prompt engineering are limited and brittle.

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🔧 Solution in this Paper:

→ Researchers fine-tuned three small LLMs (Mistral 7B, Qwen 7B, Llama 8B) on 58 sets of synthetic CBT transcripts.

→ Each transcript set contained 20 therapy sessions, designed with proper phase progression and CBT techniques.

→ Models were evaluated through simulated patient interactions using DeepSeek-V2.5 as the patient.

→ Performance was measured using a modified Cognitive Therapy Rating Scale (CTRS).

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💡 Key Insights:

→ Small LLMs can effectively learn therapeutic competencies through targeted fine-tuning

→ CBT-tuned models maintained better session structure and technique implementation

→ Models showed strong empathy but struggled with agenda adherence and deep exploration

→ Performance degraded as context length approached 4000 tokens

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📊 Results:

→ CBT-tuned models outperformed base versions by 11.33 points on CTRS (p<0.001)

→ Llama 8B performed best with mean CTRS score of 67.86 ±7.24

→ All models showed significant improvements in therapeutic competencies

→ Strongest gains in Agenda Setting, Guided Discovery, and CBT technique application