When Clients Stop Following: A Cognitive Conceptualization Diagram-driven Framework for Strategic Counseling
arXiv:2606.04389v1 Announce Type: new Abstract: Large Language Models (LLMs) show promise in psychological counseling, yet existing benchmarks rely heavily on highly cooperative simulated clients. We observe a critical counselor-following phenomenon: these clients often rapidly shift from resistance to compliance after only a few turns, creating an illusion of therapeutic progress and inflating scores under current evaluation protocols through superficial empathy. To address this evaluation mismatch, we propose a Cognitive Behavioral Therapy (CBT)-grounded resistance-aware framework. We introd
The proliferation of LLMs into sensitive applications like counseling necessitates robust evaluation metrics beyond superficial performance, highlighting an urgent need to address current benchmark limitations.
This research addresses a critical gap in AI evaluation, particularly for LLMs in high-stakes domains, by revealing how current metrics can misrepresent AI capabilities and therapeutic efficacy.
The proposed framework introduces a resistance-aware evaluation for LLMs in mental health, shifting focus from mere client compliance to a more nuanced assessment of genuine therapeutic progress and model robustness.
- · AI ethics researchers
- · Mental health tech startups
- · Patients seeking AI-enhanced therapy
- · Developers of robust LLM evaluation platforms
- · Developers of poorly validated LLM counseling tools
- · Benchmarks relying solely on cooperative client simulations
- · Early-stage AI therapy providers with superficial metrics
Improved evaluation methodologies for AI in sensitive applications like mental health become standard.
Increased trust and adoption of AI-driven mental health solutions that are validated against more rigorous, resistance-aware benchmarks.
Ethical guidelines for AI development will evolve to incorporate robust, context-sensitive performance metrics beyond simple task completion.
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Read at arXiv cs.CL