量子位·2025-05-21 10:39Core Viewpoint - Google DeepMind has introduced Gemini Diffusion, a new language model that utilizes diffusion technology to significantly enhance text generation speed and quality compared to traditional autoregressive models [1][4][9]. Group 1: Technology and Performance - Gemini Diffusion can generate text at a speed of 2000 tokens per second, which is faster than the previous model, Gemini 2.0 Flash-Lite [7][11]. - The model employs a unique approach of refining noise to learn output generation, allowing for rapid iterations and error correction during the generation process [6][10][15]. - Unlike traditional models that generate one token at a time, Gemini Diffusion can generate entire blocks of tokens simultaneously, resulting in more coherent responses [14][9]. Group 2: Benchmarking and Comparisons - Benchmark tests show that Gemini Diffusion performs comparably to larger models, with specific metrics indicating it outperforms Gemini 2.0 Flash-Lite in several coding tasks [8]. - For example, in the HumanEval benchmark, Gemini Diffusion achieved a score of 76.0%, slightly higher than Gemini 2.0 Flash-Lite's 75.8% [8]. Group 3: Implications and Future Directions - The introduction of diffusion technology in language models suggests a potential shift towards more hybrid models in the future, as seen in similar research by other institutions [19][20]. - The ability to perform non-causal reasoning during text generation opens up new possibilities for complex problem-solving tasks that traditional autoregressive models struggle with [16][17].