用多模型协作翻译高质量日语心理咨询数据，提升中英版本准确率。
Multilingual KokoroChat: A Multi-LLM Ensemble Translation Method for Creating a Multilingual Counseling Dialogue Dataset
- 采用多大模型生成候选译文，再由单一模型综合优劣择优
- 人工评估显示其译文显著优于任一单模型输出
- 适合心理对话、跨语言研究等对准确性要求高的场景
为解决高质量公开心理咨询对话数据集稀缺的问题，我们通过将大规模日语人工撰写的心理咨询语料库KokoroChat翻译成英文和中文，构建了Multilingual KokoroChat。由于不同输入内容最优翻译模型各异，单一大模型难以持续保证最高质量。在敏感的心理咨询领域，翻译保真度至关重要。为此，我们提出一种新型多大模型集成方法：先由多个不同大模型生成多样候选译文，再由一个模型基于对各候选优劣的分析生成最终高质量译文。通过人工偏好测试验证，该方法生成的译文在各项指标上均优于任何单一顶尖大模型。Multilingual KokoroChat数据集已开源，地址为https://github.com/UEC-InabaLab/MultilingualKokoroChat。
原文摘要 · Abstract (English)
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 https://github.com/UEC-InabaLab/MultilingualKokoroChat.
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