LLM Program Optimization via Retrieval Augmented Search

• URL: http://arxiv.org/abs/2501.18916v1
• Date: Fri, 31 Jan 2025 06:34:47 GMT
• Title: LLM Program Optimization via Retrieval Augmented Search
• Authors: Sagnik Anupam, Alexander Shypula, Osbert Bastani,
• Abstract summary: We propose a blackbox adaptation method called Retrieval Augmented Search (RAS) that performs beam search over candidate optimizations. We show that RAS performs 1.8$times$ better than prior state-of-the-art blackbox adaptation strategies. We also propose a method called AEGIS for improving interpretability by decomposing training examples into "atomic edits"
• Score: 71.40092732256252
• License:
• Abstract: With the advent of large language models (LLMs), there has been a great deal of interest in applying them to solve difficult programming tasks. Recent work has demonstrated their potential at program optimization, a key challenge in programming languages research. We propose a blackbox adaptation method called Retrieval Augmented Search (RAS) that performs beam search over candidate optimizations; at each step, it retrieves in-context examples from a given training dataset of slow-fast program pairs to guide the LLM. Critically, we find that performing contextual retrieval based on an LLM-generated natural language description significantly outperforms retrieval based on the source code. In addition, we propose a method called AEGIS for improving interpretability by decomposing training examples into "atomic edits" that are significantly more incremental in nature. We show that RAS performs 1.8$\times$ better than prior state-of-the-art blackbox adaptation strategies, and that AEGIS performs 1.37$\times$ better while performing significantly smaller edits.

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