Papers by Alexander Shypula

2 papers
LLM Program Optimization via Retrieval Augmented Search (2026.findings-acl)

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Challenge: Recent work shows that large language models have difficulty with program optimization out-of-the-box.
Approach: They propose a blackbox adaptation method that performs beam search over candidate optimizations by a training dataset.
Outcome: The proposed method outperforms retrieval based on the source code in a number of ways.
Explain-then-translate: an analysis on improving program translation with self-generated explanations (2023.findings-emnlp)

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Challenge: Using self-generated natural language explanations improves zero-shot performance by 12% on average.
Approach: They propose to use self-generated natural language explanations as an intermediate step for code-to-code translation with language models.
Outcome: The proposed approach improves zero-shot performance by 12% on average . the proposed approach is not evaluated on a broader set of languages including low-resource languages.

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