  Large Language Models (LLMs) have already become quite proficient at solving
simpler programming tasks like those in HumanEval or MBPP benchmarks. However,
solving more complex and competitive programming tasks is still quite
challenging for these models - possibly due to their tendency to generate
solutions as monolithic code blocks instead of decomposing them into logical
sub-tasks and sub-modules. On the other hand, experienced programmers
instinctively write modularized code with abstraction for solving complex
tasks, often reusing previously developed modules. To address this gap, we
propose CodeChain, a novel framework for inference that elicits modularized
code generation through a chain of self-revisions, each being guided by some
representative sub-modules generated in previous iterations. Concretely,
CodeChain first instructs the LLM to generate modularized codes through
chain-of-thought prompting. Then it applies a chain of self-revisions by
iterating the two steps: 1) extracting and clustering the generated sub-modules
and selecting the cluster representatives as the more generic and re-usable
implementations, and 2) augmenting the original chain-of-thought prompt with
these selected module-implementations and instructing the LLM to re-generate
new modularized solutions. We find that by naturally encouraging the LLM to
reuse the previously developed and verified sub-modules, CodeChain can
significantly boost both modularity as well as correctness of the generated
solutions, achieving relative pass@1 improvements of 35% on APPS and 76% on
CodeContests. It is shown to be effective on both OpenAI LLMs as well as
open-sourced LLMs like WizardCoder. We also conduct comprehensive ablation
studies with different methods of prompting, number of clusters, model sizes,
program qualities, etc., to provide useful insights that underpin CodeChain's
success.
