  We introduce Syntax-Aware Fill-In-the-Middle (SAFIM), a new benchmark for
evaluating Large Language Models (LLMs) on the code Fill-in-the-Middle (FIM)
task. This benchmark focuses on syntax-aware completions of program structures
such as code blocks and conditional expressions, and includes 17,720 examples
from multiple programming languages, sourced from recent code submissions after
April 2022 to minimize data contamination. SAFIM provides a robust framework
with various prompt designs and novel syntax-aware post-processing techniques,
facilitating accurate and fair comparisons across LLMs. Our comprehensive
evaluation of 15 LLMs shows that FIM pretraining not only enhances FIM
proficiency but also improves Left-to-Right (L2R) inference using LLMs. Our
findings challenge conventional beliefs and suggest that pretraining methods
and data quality have more impact than model size. SAFIM thus serves as a
foundational platform for future research in effective pretraining strategies
for code LLMs. The evaluation toolkit and dataset are available at
https://github.com/gonglinyuan/safim, and the leaderboard is available at
https://safimbenchmark.com.
