OpenRTLSet supplies 131k+ Verilog samples with AI-generated descriptions to enable fine-tuning of LLMs for hardware module design.
Stelocoder: a decoder-only llm for multi-language to pyth on code translation
5 Pith papers cite this work. Polarity classification is still indexing.
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Adversarial perturbations possess an inherently low-rank structure that enables more efficient and effective black-box adversarial attacks via subspace projection.
ArkTrans achieves up to 90.67% compilable ArkUI translations from KJC/SwiftUI using heuristic LLM guidance and empirical post-fixing rules, versus 0% for direct or one-shot prompting on a 100-sample benchmark.
Deterministic orchestration matches LLM-controlled methods in COBOL-to-Python translation accuracy but improves worst-case robustness, reduces run-to-run variability, and cuts token consumption by up to 3.5 times.
A large-scale study finds that many LLM code translation failures are false negatives due to improper evaluation configurations rather than incorrect translations.
citing papers explorer
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OpenRTLSet: A Fully Open-Source Dataset for Large Language Model-based Verilog Module Design
OpenRTLSet supplies 131k+ Verilog samples with AI-generated descriptions to enable fine-tuning of LLMs for hardware module design.
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Low Rank Adaptation for Adversarial Perturbation
Adversarial perturbations possess an inherently low-rank structure that enables more efficient and effective black-box adversarial attacks via subspace projection.
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Porting Declarative UI to HarmonyOS: A Heuristic-guided LLM Approach
ArkTrans achieves up to 90.67% compilable ArkUI translations from KJC/SwiftUI using heuristic LLM guidance and empirical post-fixing rules, versus 0% for direct or one-shot prompting on a 100-sample benchmark.
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Deterministic vs. LLM-Controlled Orchestration for COBOL-to-Python Modernization
Deterministic orchestration matches LLM-controlled methods in COBOL-to-Python translation accuracy but improves worst-case robustness, reduces run-to-run variability, and cuts token consumption by up to 3.5 times.
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Beyond Translation Accuracy: Addressing False Failures in LLM-Based Code Translation
A large-scale study finds that many LLM code translation failures are false negatives due to improper evaluation configurations rather than incorrect translations.