Verified75% confidenceFactExact time
GLM uses an autoregressive blank-filling training objective, randomly masking text segments and requiring the model to autoregressively restore them.
3
Sources
75%
Confidence
Long-term
Relevance
7/21/2026
First Seen
Sources
Related Claims
VerifiedThe GLM series is a large language model jointly developed by Tsinghua University and Zhipu AI, built on the Autoregressive Blank Infilling training objective.74% similarUnverifiedIf LLM judges that are blind to omissions are widely used for scoring, benchmarking, or guiding model training (e.g., RLHF, RLAIF), the entire optimization loop may be misled, causing models to learn to generate content that appears complete but is actually incomplete.72% similarVerifiedGLM unifies the pre-training objectives of natural language understanding and generation through Autoregressive Blank Infilling.72% similarUnverifiedMasked Diffusion Language Modeling (MDLM) operates on discrete token spaces, where the forward process randomly replaces tokens with [MASK] and the reverse process trains the model to progressively restore the masked tokens.71% similar
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