Michael Brenndoerfer | Data & AI, Private Equity, Technology

Michael Brenndoerfer

ALBERT › Performance Benchmarks

Michael BrenndoerferUpdated July 19, 20255 min read

Language AI HandbookAll chapters

Part 24 of 67BERT and Variants

Chapter 174ALBERT › Performance Benchmarks

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The ALBERT paper demonstrated that smaller models could match or exceed BERT's performance. On GLUE benchmark tasks, ALBERT-xxLarge achieved state-of-the-art results despite having fewer unique parameters than BERT-Large.

The key finding: representation quality depends more on model width and depth of computation than on parameter count. ALBERT's shared parameters still learn effective transformations when applied repeatedly.

In[44]:

Code

# Reported performance from the ALBERT paper (GLUE dev set average)
benchmark_results = {
    "BERT-Base": {"params": 110, "glue_avg": 80.5, "squad_v2": 76.3},
    "BERT-Large": {"params": 340, "glue_avg": 82.3, "squad_v2": 81.8},
    "ALBERT-Base": {"params": 12, "glue_avg": 80.1, "squad_v2": 80.0},
    "ALBERT-Large": {"params": 18, "glue_avg": 82.4, "squad_v2": 82.3},
    "ALBERT-xLarge": {"params": 60, "glue_avg": 85.5, "squad_v2": 86.1},
    "ALBERT-xxLarge": {"params": 235, "glue_avg": 89.4, "squad_v2": 89.8},
}

Out[45]:

Console

Model Performance Comparison:
-----------------------------------------------------------------
Model              Parameters   GLUE Avg     SQuAD v2.0  
-----------------------------------------------------------------
BERT-Base               110M       80.5         76.3
BERT-Large              340M       82.3         81.8
ALBERT-Base              12M       80.1         80.0
ALBERT-Large             18M       82.4         82.3
ALBERT-xLarge            60M       85.5         86.1
ALBERT-xxLarge          235M       89.4         89.8

A useful metric for comparing models is parameter efficiency, which measures performance per million parameters. This reveals which architectures extract the most value from their parameters:

Out[46]:

Visualization

Bar chart showing GLUE score per million parameters for each model. — Parameter efficiency (GLUE score per million parameters) across models. ALBERT-Base achieves the highest efficiency, extracting 6.7 GLUE points per million parameters compared to BERT-Base's 0.73. Even ALBERT-xxLarge, while less efficient than smaller ALBERTs, still outperforms BERT models in efficiency.

Out[47]:

Visualization

Scatter plot showing GLUE benchmark scores vs parameter count for various BERT and ALBERT models. — Performance vs parameters for BERT and ALBERT models. ALBERT achieves better performance-to-parameter ratios, with ALBERT-xLarge matching BERT-Large performance at ~6x fewer parameters. ALBERT-xxLarge sets new records despite using shared parameters.

We can also compare performance across both GLUE and SQuAD benchmarks simultaneously:

Out[48]:

Visualization

Scatter plot with GLUE score on x-axis and SQuAD score on y-axis, showing model performance trajectories. — Reported GLUE and SQuAD v2.0 scores for BERT and ALBERT configurations. Family trajectories connect increasing model sizes, while marker area encodes the logarithm of parameter count; the ALBERT trajectory reaches higher scores with fewer parameters at several operating points.