Artificial intelligence (AI) Large Language Models (LLMs) are increasingly being used in mental health training and therapeutic interventions. This narrative review explores how LLMs can support simulation-based training for mental health practitioners by simulating patient interactions. Key topics include the use of co-production techniques, cost considerations, and the role of Retrieval-Augmented Generation (RAG) in enhancing training accuracy. The review highlights the importance of evaluation methods and expert-guided enhancements. Although LLMs offer valuable training opportunities, they should complement, rather than replace traditional face-to-face training methods. Collaboration across disciplines is essential in refining AI chatbots and enhancing their effectiveness in therapeutic training. In conclusion, the integration of AI in mental health practitioner training shows promise for addressing the growing demands within mental health care. However further research is required to develop standardized approaches and improve the realism of AI simulations.

Accurately representing synonymy using distributional similarity requires large volumes of data to reliably represent infrequent words. However, the naïve nearest-neighbour approach to comparing context vectors extracted from large corpora scales poorly (O(n2) in the vocabulary size).In this paper, we compare several existing approaches to approximating the nearest-neighbour search for distributional similarity. We investigate the trade-off between efficiency and accuracy, and find that SASH (Houle and Sakuma, 2005) provides the best balance.