arXiv Machine Learning

LLM-Based Embeddings for Program Analysis and Optimization

arXiv:2608. 07894v1 Announce Type: new Abstract: Recent advances have highlighted the potential of machine learning, particularly Large Language Models (LLMs), for analyzing and optimizing programs.

arXiv AI

23h ago

LSem2Vec: A Simple yet Effective Two-Stage Approach for Source Code Embedding

arXiv:2409. 14644v4 Announce Type: replace-cross Abstract: The advent of large language models (LLMs) has significantly advanced artificial intelligence in software engineering, with source code embeddings playing a crucial role in tasks such as source code clone detection and source code clustering.

By Zixiang Xian, Chenhui Cui, Rubing Huang, Chunrong Fang, Zhenyu Chen

llms rag fine-tuning benchmarks

arXiv AI

Jun 16

FasterPy: An LLM-based Code Execution Efficiency Optimization Framework

arXiv:2512. 22827v2 Announce Type: replace-cross Abstract: Code often suffers from performance bugs.

By Yue Wu, Minghao Han, Ruiyin Li, Peng Liang, Amjed Tahir, Zengyang Li, Qiong Feng, Mojtaba Shahin

llms rag fine-tuning benchmarks

arXiv AI

Jul 13

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity

arXiv:2509. 23449v2 Announce Type: replace Abstract: Binary code similarity detection is a core task in reverse engineering.

By Charles E. Gagnon, Steven H. H. Ding, Philippe Charland, Benjamin C. M. Fung

llms rag agents benchmarks safety

arXiv AI

Jun 4

CodegenBench: Can LLMs Write Efficient Code Across Architectures?

arXiv:2606. 04023v1 Announce Type: cross Abstract: While large language models (LLMs) have been extensively evaluated on code generation tasks for general-purpose programming and GPU-accelerated environments (e.

By Jie Li, Wenzhao Wu, Junqi Hu, Qinrui Zheng, Bowen Wu, Juepeng Zheng, Yutong Lu, Haohuan Fu

llms benchmarks

arXiv AI

Jun 10

PromptEmbedder: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting

arXiv:2605. 28066v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable efficacy in text embedding, yet current adaptation methods like LoRA face significant bottlenecks in computational efficiency and cross-architecture transferability.

By Yu-Che Tsai, Kuan-Yu Chen, Yuan-Hao Chen, Yu-Han Chang, Ching-Yu Tsai, Yu-Hsiang Chuang, Shou-De Lin

llms rag fine-tuning benchmarks safety

arXiv AI

1d ago

Efficient Code Embeddings from Code Generation Models

arXiv:2508. 21290v2 Announce Type: replace-cross Abstract: jina-code-embeddings is a novel code embedding model suite designed to retrieve code from natural language queries, perform technical question-answering, and identify semantically similar code snippets across programming languages.

By Daria Kryvosheieva, Saba Sturua, Michael G\"unther, Han Xiao

rag benchmarks

arXiv Machine Learning

Jun 8

Skip a Layer or Loop It? Learning Program-of-Layers in LLMs

arXiv:2606. 06574v1 Announce Type: new Abstract: Large language models (LLMs) perform inference by following a fixed depth and order, non-recurrent execution of all layers.

By Ziyue Li, Yang Li, Tianyi Zhou

llms benchmarks

arXiv Machine Learning

Jun 25

LLM Program Optimization via Retrieval Augmented Search

arXiv:2501. 18916v2 Announce Type: replace Abstract: Recent work has demonstrated the potential of large language models (LLMs) for program optimization, a key challenge in programming languages.

By Sagnik Anupam, Alexander Shypula, Osbert Bastani

llms rag benchmarks safety

arXiv AI

Aug 11

Effect of Abstractions and Prompting Strategies on LLM-Guided High-Performance Optimizations

arXiv:2608. 08085v1 Announce Type: cross Abstract: Code performance optimization is a vital aspect of modern software development, as it enables faster response times and reduced resource usage.

By Ji\v{r}\'i Klepl, Maty'a\v{s} Brabec, Martin Kruli\v{s}

llms benchmarks

arXiv AI

Jun 24

SemChunk-C: Semantic Segmentation for C Code

arXiv:2606. 23697v1 Announce Type: cross Abstract: Semantic segmentation of code written in a C-family language remains a challenging problem, due to the language's complex syntax, macro expansion, and irregular structural patterns.

By Boris Nazarov, Darya Frolova, Shaked Leibzirer, Pavel Kisilev

llms rag computer-vision benchmarks

arXiv AI

Jul 31

ARC-Encoder: learning compressed text representations for large language models

arXiv:2510. 20535v2 Announce Type: replace-cross Abstract: Recent techniques such as retrieval-augmented generation or chain-of-thought reasoning have led to longer contexts and increased inference costs.

By Hippolyte Pilchen, Edouard Grave, Patrick P\'erez

llms rag fine-tuning benchmarks

arXiv AI

Jun 6

Beyond Code Pairs: Dialogue-Based Data Generation for LLM Code Translation

arXiv:2512. 03086v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in code translation, yet their performance deteriorates in low-resource programming domains such as Fortran and emerging frameworks like CUDA, where high-quality parallel data are scarce.

By Le Chen, Nuo Xu, Winson Chen, Bin Lei, Pei-Hung Lin, Dunzhi Zhou, Rajeev Thakur, Caiwen Ding, Ali Jannesari, Chunhua Liao

llms fine-tuning

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