EpiCoder's tree-based approach lets it scale from writing functions to entire code repositories.
EpiCoder introduces a feature tree framework for code generation that captures semantic relationships between code elements, enabling more complex and diverse code synthesis than traditional snippet-based methods.
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https://arxiv.org/abs/2501.04694
Original Problem 🤔:
Current code instruction tuning methods rely on rigid code snippets, limiting the complexity and diversity of synthesized data for training LLMs. This restricts their ability to handle real-world programming scenarios.
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Solution in this Paper 🔧:
→ The framework builds feature trees from code by extracting semantic relationships between elements, unlike Abstract Syntax Trees that only capture syntax
→ Features are organized hierarchically and evolved through depth and breadth expansion to increase diversity
→ Subtrees are sampled with controlled complexity to generate code ranging from functions to multi-file projects
→ The framework adjusts feature sampling probabilities to prioritize underrepresented knowledge areas
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Key Insights 💡:
→ Semantic relationships between code elements are more valuable than pure syntax for generating diverse code
→ Hierarchical feature organization enables controlled scaling of code complexity
→ Feature evolution is more efficient than evolving individual code snippets
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Results 📊:
→ EpiCoder-Qwen-7B achieves SOTA performance on 5 function-level benchmarks
→ Successfully generates complex repository-level code with 50+ files
→ Shows 2.55x improvement in unique operators and 20.99x in unique operands vs baselines
→ Demonstrates superior performance on file-level XFileDep benchmark
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