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About this paper
Zhaoling Chen, Xiangru Tang, Gangda Deng, Fang Wu, Jialong Wu, Zhiwei Jiang, Viktor Prasanna, Arman Cohan and 1 more
LocAgent introduces a framework that guides large language model agents through a heterogeneous graph representation of codebases, enabling precise code localization for various software maintenance tasks. This approach achieves state-of-the-art accuracy on benchmarks like SWE-Bench-Lite and LOC-BENCH, while fine-tuned open-source models reduce operational costs by up to 86% compared to proprietary LLMs.
Key insights
- Integrating a comprehensive graph representation with LLM agents enables superior multi-hop reasoning, crucial for tracing complex dependencies that span across different files and modules.
- Specialized, context-aware tools that allow LLMs to perform targeted entity search, graph traversal, and content retrieval are essential for efficient codebase navigation and information synthesis.
- High-quality fine-tuning data derived from successful agent interactions allows open-source models to achieve performance comparable to proprietary LLMs at substantially lower inference costs.
Summary by alphaXiv.
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