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GLM-130B: An Open Bilingual Pre-trained Model

ICLR 2023 · Large language models › Language, safety and interpretability · approach: LLM pruning and scaling data · 293 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
INT4 weight quantization without post-training (activations kept FP16) (ablation)GLM-130B · LAMBADA zero-shotaccuracy: 80.21 -> 79.47 (-0.74)No clear effect
INT4 weight quantization without post-training (activations kept FP16) (ablation)GLM-130B · MMLU 5-shotaccuracy: 44.75 -> 44.80 (+0.05)No clear effect
INT8 weight quantization (activations FP16) (ablation)GLM-130B · LAMBADA zero-shotaccuracy: 80.21 -> 80.21No clear effect
Embedding layer gradient shrink (alpha=0.1) (ablation)GLM-130B training · Training stability (loss spikes)loss spikes / divergenceHelped
DeepNorm Post-LN initialization instead of Pre-LN/Sandwich-LN (ablation)GLM-130B training · Training stabilitytraining collapseHelped
RoPE instead of ALiBi for positional encoding (base model) (ablation)GLM-Base 110M · Test PPLtest perplexity: 24.14 (ALiBi) -> 22.95 (RoPE)Helped
GeGLU FFN added on top of RoPE (base model) (ablation)GLM-Base 110M · Test PPLtest perplexity: 22.95 -> 22.31Helped
Embedding-layer gradient shrink with norm-based FP16 vs embedding norm (BLOOM-style) and PB-RelaxGLM-130B training · Training stabilityloss spikesMixed
GLM-130B vs GPT-3 175B on LAMBADA zero-shotGLM-130B · LAMBADA zero-shotaccuracy: +4.0 points (80.2 vs 76.2)Helped
Bidirectional GLM backbone vs unidirectional attention variant (ablation)GLM-130B · LAMBADA zero-shotaccuracy: 80.2 bidirectional vs 75.3 unidirectionalHelped
GLM-130B vs OPT-175B and GPT-3 on CrowS-PairsGLM-130B · CrowS-Pairs overallbias score (lower better): 65.8 vs 67.2 (GPT-3) and 69.5 (OPT-175B)Helped
GLM-130B vs GPT-3 on ETHOS multiclass few-shotGLM-130B · ETHOS hate speech detectionF1: 85.8 vs 67.2 (GPT-3), 81.2 (OPT-175B)Helped

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