Traditional psychological counseling, often conducted in person, remains limited to individuals actively seeking help for psychological concerns. In contrast, online automated counseling presents a viable option for those hesitant to pursue therapy due to stigma or shame. Cognitive Behavioral Therapy (CBT), a widely practiced approach in psychological counseling, aims to help individuals identify and correct cognitive distortions contributing to negative emotions and behaviors. The emergence of LLMs has opened new possibilities for automating CBT diagnosis and treatment. However, current LLM-based CBT systems face challenges such as fixed structural frameworks, which limit adaptability and self-optimization, and repetitive response patterns that provide generic, unhelpful suggestions.
Recent advancements in AI have introduced frameworks like CBT-LLM, which employs prompt-based learning, and CoCoA, which integrates memory mechanisms for retrieval-augmented generation. These systems aim to identify and address cognitive distortions in user statements while enhancing the depth and relevance of therapeutic interactions. Despite their potential, existing methods often lack personalization, adaptability to changing user needs, and a nuanced understanding of dynamic therapeutic processes. To bridge these gaps, ongoing research uses annotated datasets, ontologies, and advanced LLMs to develop context-aware CBT systems that mimic human cognitive processes.
Researchers from the Shenzhen Key Laboratory for High-Performance Data Mining, Shenzhen Institutes of Advanced Technology, the Chinese Academy of Sciences, and several other institutions developed AutoCBT, an autonomous multi-agent framework designed for CBT in single-turn psychological consultations. Utilizing Quora-like and YiXinLi models, AutoCBT integrates dynamic routing and memory mechanisms to improve response quality and adaptability. The framework undergoes structured reasoning and editing to generate high-quality, context-aware outputs. Evaluated on a bilingual dataset, it outperforms traditional LLM-based systems, addressing challenges like dynamic routing, supervisory mechanisms, and Llama’s over-protection issue.
AutoCBT is a versatile framework designed for multi-agent systems in CBT, comprising a Counsellor Agent (interface), Supervisor Agents, communication topology, and routing strategies. The Counsellor Agent, powered by LLMs, interacts with users and seeks input from Supervisor Agents to generate confident, high-quality responses. Agents feature memory mechanisms for short-term and long-term storage, and routing strategies like unicast and broadcast enable dynamic communication. AutoCBT incorporates CBT principles—empathy, belief identification, reflection, strategy, and encouragement—mapped to specific Supervisor Agents. Its effectiveness was validated using a bilingual dataset combining PsyQA and TherapistQA, categorized and augmented with cognitive distortion examples.
In online psychological counseling, LLMs like Qwen-2.5-72B and Llama-3.1-70B were evaluated for handling emotional nuances and instruction adherence. AutoCBT, a two-stage framework, outperformed Generation and PromptCBT by incorporating dynamic routing and supervisory mechanisms, achieving higher scores across empathy, cognitive distortion handling, and response relevance. AutoCBT’s iterative approach enhanced its draft responses, which were validated by automatic and human evaluations. Challenges included routing conflicts, role confusion, and redundant feedback loops, mitigated through design adjustments. Llama’s over-caution led to frequent refusals on sensitive topics, unlike Qwen, which responded comprehensively, highlighting the importance of balance in model sensitivity.
In conclusion, AutoCBT is an innovative multi-agent framework designed for CBT-based psychological counseling. By integrating dynamic routing and supervisory mechanisms, AutoCBT addresses limitations in traditional LLM-based counseling, significantly enhancing response quality and effectiveness in identifying and addressing cognitive distortions. AutoCBT achieves superior dialogue quality through its adaptive and autonomous design compared to static, prompt-based systems. Challenges in LLMs’ semantic understanding and instruction adherence were identified and mitigated through targeted solutions. Leveraging bilingual datasets and models, the framework demonstrates its potential to deliver high-quality, automated counseling services. It offers a scalable alternative for individuals hesitant to pursue traditional therapy due to stigma.
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