Synthetic Programming Elicitation and Repair for Text-to-Code in Very Low-Resource Programming Languages
CoRR(2024)
摘要
Recent advances in large language models (LLMs) for code applications have
demonstrated remarkable zero-shot fluency and instruction following on
challenging code related tasks ranging from test case generation to
self-repair. Unsurprisingly, however, models struggle to compose syntactically
valid programs in programming languages unrepresented in pre-training, referred
to as very low-resource Programming Languages (VLPLs). VLPLs appear in crucial
settings including domain-specific languages for internal to tools and
tool-chains and legacy languages. Inspired by an HCI technique called natural
program elicitation, we propose designing an intermediate language that LLMs
“naturally” know how to use and which can be automatically compiled to the
target VLPL. Specifically, we introduce synthetic programming elicitation and
compilation (SPEAK), an approach that enables LLMs to generate syntactically
valid code even for VLPLs. We empirically evaluate the performance of SPEAK in
a case study and find that, compared to existing retrieval and fine-tuning
baselines, SPEAK produces syntactically correct programs more frequently
without sacrificing semantic correctness.
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