Stay on topic with Classifier-Free Guidance
Authors: name: Guillaume Sanchez, name: Honglu Fan, name: Alexander Spangher, name: Elad Levi, name: Pawan Sasanka Ammanamanchi, name: Stella Biderman

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.