2023/06/30 by Guillaume Sanchez, Honglu Fan, Sanchez, Guillaume +9 · 2 voices · 18 citations
Computer Science · Decision Sciences · Mathematics · #Artificial intelligence #Classifier (UML) #Coherence (philosophical gambling strategy) #Computer science #Consistency (knowledge bases) #Inference #Machine Learning and Data Classification #Machine learning #Machine translation #Mathematics #Natural language processing #Scientific Computing and Data Management #Statistics #Topic Modeling #cs.CL #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2306.17806
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/06/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.