Single Language

Research on single language models focuses on improving their performance and safety across various tasks, including text generation, question answering, and information retrieval. Current efforts concentrate on enhancing model alignment with human preferences, addressing biases and unfairness in multilingual contexts, and developing efficient training methods like adapter-based fine-tuning and direct preference optimization. These advancements are crucial for building reliable and ethical AI systems, impacting fields ranging from natural language processing to multimodal applications like image captioning and music generation. The ultimate goal is to create robust, versatile single language models that are both powerful and safe.