The xLLMs project is a growing suite of multilingual and multimodal dialogue datasets designed to train and evaluate advanced conversational LLMs. Each dataset focuses on a specific capability β from long-context reasoning and factual grounding to STEM explanations, math Q&A, and polite multilingual interaction.
π¬ Highlight: xLLMs β Dialogue Pubs A large-scale multilingual dataset built from document-guided synthetic dialogues (Wikipedia, WikiHow, and technical sources). Itβs ideal for training models on long-context reasoning, multi-turn coherence, and tool-augmented dialogue across 9 languages. π lamhieu/xllms_dialogue_pubs
π§ Designed for: - Long-context and reasoning models - Multilingual assistants - Tool-calling and structured response learning
All datasets are open for research and development use β free, transparent, and carefully curated to improve dialogue model quality.