Multilingual KokoroChat: A Multi-LLM Ensemble Translation Method for Creating a Multilingual Counseling Dialogue Dataset

View PDF HTML (experimental) Abstract:To address the critical scarcity of high-quality, publicly available counseling dialogue datasets, we created Multilingual KokoroChat by translating KokoroChat, a large-scale manually authored Japanese counseling corpus, into both English and Chinese. A key challenge in this process is that the optimal model for translation varies by input, making it impossible for any single model to consistently guarantee the highest quality. In a sensitive domain like counseling, where the highest possible translation fidelity is essential, relying on a single LLM is therefore insufficient. To overcome this challenge, we developed and employed a novel multi-LLM ensemble method. Our approach first generates diverse hypotheses from multiple distinct LLMs. A single LLM then produces a high-quality translation based on an analysis of the respective strengths and weaknesses of all presented hypotheses. The quality of ``Multilingual KokoroChat'' was rigorously validated through human preference studies. These evaluations confirmed that the translations produced by our ensemble method were preferred from any individual state-of-the-art LLM. This strong preference confirms the superior quality of our method's outputs. The Multilingual KokoroChat is available at this https URL. Comments: 12 pages, 8 figures, Accepted to LREC 2026 Subjects: Computation and Language (cs.CL) Cite as: arXiv:2603.22913 [cs.CL] (or arXiv:2603.22913v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2603.22913 arXiv-issued DOI via DataCite Submission history From: Ryoma Suzuki [view email] [v1] Tue, 24 Mar 2026 08:02:24 UTC (1,924 KB) [v2] Mon, 6 Apr 2026 11:32:09 UTC (1,924 KB)