Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Masking
Classifier-Free Guidance (CFG) significantly enhances controllability in
generative models by interpolating conditional and unconditional predictions.
However, standard CFG often employs a static unconditional input, which can be
suboptimal for iterative generation processes where model uncertainty varies
dynamically. We introduce Adaptive Classifier-Free Guidance (A-CFG), a novel
method that tailors the unconditional input by leveraging the model's
instantaneous predictive confidence. At each step of an iterative (masked)
diffusion language model, A-CFG identifies tokens in the currently generated
sequence for which the model exhibits low confidence. These tokens are
temporarily re-masked to create a dynamic, localized unconditional input. This
focuses CFG's corrective influence precisely on areas of ambiguity, leading to
more effective guidance. We integrate A-CFG into a state-of-the-art masked
diffusion language model and demonstrate its efficacy. Experiments on diverse
language generation benchmarks show that A-CFG yields substantial improvements
over standard CFG, achieving, for instance, a 3.9 point gain on GPQA. Our work
highlights the benefit of dynamically adapting guidance mechanisms to model
uncertainty in iterative generation.
683919611186f2cbf3ed2267
Pengxiang Li
683919611186f2cbf3ed2268
Shilin Yan
683919611186f2cbf3ed2269
Joey Tsai
683919611186f2cbf3ed226a
Renrui Zhang
683919611186f2cbf3ed226b
Ruichuan An
683919611186f2cbf3ed226c
Ziyu Guo
683919611186f2cbf3ed226d
Xiaowei Gao
Adaptive Classifier-Free Guidance via Dynamic Low-Confidence Masking
Classifier-Free Guidance (CFG) significantly enhances controllability in
generative models by interpolating conditional and unconditional predictions.
However, standard CFG often employs a static unconditional input, which can be
suboptimal for iterative generation processes where model uncertainty varies
dynamically. We introduce Adaptive Classifier-Free Guidance (A-CFG), a novel
method that tailors the unconditional input by leveraging the model's
instantaneous predictive confidence. At each step of an iterative (masked)
diffusion language model, A-CFG identifies tokens in the currently generated
sequence for which the model exhibits low confidence. These tokens are
temporarily re-masked to create a dynamic, localized unconditional input. This
focuses CFG's corrective influence precisely on areas of ambiguity, leading to
more effective guidance. We integrate A-CFG into a state-of-the-art masked
diffusion language model and demonstrate its efficacy. Experiments on diverse
language generation benchmarks show that A-CFG yields substantial improvements
over standard CFG, achieving, for instance, a 3.9 point gain on GPQA. Our work
highlights the benefit of dynamically adapting guidance mechanisms to model
uncertainty in iterative generation.
Adaptive Classifier-Free Guidance (A-CFG) dynamically adjusts the guidance in masked diffusion language models by focusing on areas of low model confidence, leading to significant improvements in language generation performance.
Classifier-Free Guidance (CFG) Adaptive Classifier-Free Guidance (A-CFG) masked diffusion language model predictive confidence GPQA