APIKG4Syn synthesizes API-oriented training data via knowledge graphs and Monte Carlo search to fine-tune a 7B model that reaches 25% pass@1 on HarmonyOS code generation, beating untuned GPT-4o at 17.59%.
Context-aware code summary generation.arXiv preprint arXiv:2408.09006, 2024
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Single-agent RAG pipeline matches multi-agent lexical quality for README generation while cutting token consumption by 86% and doubling speed, with developer-guided planning yielding the highest overall quality.
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Knowledge-Graph-Driven Data Synthesis for Low-Resource Software Development: A HarmonyOS Case Study
APIKG4Syn synthesizes API-oriented training data via knowledge graphs and Monte Carlo search to fine-tune a 7B model that reaches 25% pass@1 on HarmonyOS code generation, beating untuned GPT-4o at 17.59%.
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The Illusion of Agentic Complexity in README.md Generation: Evaluating Single-Agent vs. Multi-Agent RAG Systems
Single-agent RAG pipeline matches multi-agent lexical quality for README generation while cutting token consumption by 86% and doubling speed, with developer-guided planning yielding the highest overall quality.