标题： 专注于无分类器指导 显示英文标题

标题： Stay on topic with Classifier-Free Guidance

摘要： 无分类器引导（CFG）最近在文本到图像生成中出现，作为一种轻量级技术，以鼓励生成内容遵循提示。 在本工作中，我们证明CFG可以广泛作为纯语言建模中的推理时技术使用。 我们表明CFG（1）在一系列任务中提高了Pythia、GPT-2和LLaMA系列模型的性能：问答、推理、代码生成和机器翻译，在LAMBADA任务上使用LLaMA-7B超过了PaLM-540B，达到最先进水平；（2）带来的改进相当于参数量两倍的模型；（3）可以与其他推理时方法如思维链和自我一致性结合使用，在困难任务中进一步提高性能；（4）可以用于提高助手在具有挑战性的形式驱动和内容驱动提示中的忠实度和连贯性：在一项人类评估中，我们显示75%的偏好选择使用CFG的GPT4All而非基线。 显示英文摘要

摘要： Classifier-Free Guidance (CFG) has recently emerged in text-to-image generation as a lightweight technique to encourage prompt-adherence in generations. In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG (1) improves the performance of Pythia, GPT-2 and LLaMA-family models across an array of tasks: Q\&A, reasoning, code generation, and machine translation, achieving SOTA on LAMBADA with LLaMA-7B over PaLM-540B; (2) brings improvements equivalent to a model with twice the parameter-count; (3) can stack alongside other inference-time methods like Chain-of-Thought and Self-Consistency, yielding further improvements in difficult tasks; (4) can be used to increase the faithfulness and coherence of assistants in challenging form-driven and content-driven prompts: in a human evaluation we show a 75\% preference for GPT4All using CFG over baseline.