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About this paper
Daniele Cipollone, Egor Bogomolov, Arie van Deursen, Maliheh Izadi
TreeRanker introduces a fast, model-agnostic system for ranking code suggestions in IDEs by leveraging an off-the-shelf Language Model (LLM) during a single greedy decoding pass through a prefix tree of completion candidates. The method consistently improves ranking quality over traditional IDE methods and LLM baselines, achieving up to 30x faster inference compared to exhaustive LLM reranking.
Key insights
- High-quality semantic ranking for code completions can be effectively extracted from an LLM's next-token probabilities during a single greedy decoding pass, without the need for computationally expensive beam search or complex reranking schemes.
- The 'completion tree' and its traversal method are highly efficient, extracting sufficient ranking signals with very few decoding steps, often terminating early before fully generating the identifier.
- TreeRanker's superior ranking performance primarily stems from its novel scoring mechanism, making its effectiveness robust even without explicit constrained decoding, thereby simplifying integration.
Summary by alphaXiv.
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