Interpreting and comparing LLM evaluation results
When interpreting and comparing results across these diverse benchmarks, it’s important to consider the strengths and limitations of each metric. It is also important to consider differences in model size, training data, and fine-tuning approaches. Here’s an example of how you might visualize and compare results across multiple benchmarks:
def compare_models(model1_scores, model2_scores, benchmarks):
df = pd.DataFrame({
'Model1': model1_scores,
'Model2': model2_scores
}, index=benchmarks)
ax = df.plot(kind='bar', figsize=(12, 6), width=0.8)
plt.title('Model Comparison Across Benchmarks')
plt.xlabel('Benchmarks')
plt.ylabel('Scores...
