IRIS-14B is the first LLM trained explicitly for GIMPLE-to-LLVM IR translation and outperforms much larger models by up to 44 percentage points on real-world C code.
Ircoder: Intermediate representa- tions make language models robust multilingual code generators
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UNVERDICTED 5representative citing papers
ROSUM-MCTS applies MCTS-inspired hierarchical candidate expansion and a composite reward balancing functional correctness, local content adequacy, and fluency to improve LLM summaries of VHDL and Verilog code, outperforming baselines on eval datasets.
A survey of methods, benchmarks, and open challenges for large language models in multilingual code generation and translation.
NL specifications alone do not improve LLM code translation performance, but combining them with source code yields gains in select language pairs with no overall consistent benefit.
A systematic literature review that organizes recent work on LLMs for code generation into a taxonomy covering data curation, model advances, evaluations, ethics, environmental impact, and applications, with benchmark comparisons.
citing papers explorer
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LLM Translation of Compiler Intermediate Representation
IRIS-14B is the first LLM trained explicitly for GIMPLE-to-LLVM IR translation and outperforms much larger models by up to 44 percentage points on real-world C code.
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ROSUM-MCTS: Monte Carlo Tree Search-Inspired HDL Code Summarization with Structural Rewards
ROSUM-MCTS applies MCTS-inspired hierarchical candidate expansion and a composite reward balancing functional correctness, local content adequacy, and fluency to improve LLM summaries of VHDL and Verilog code, outperforming baselines on eval datasets.
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Large Language Models for Multilingual Code Intelligence: A Survey
A survey of methods, benchmarks, and open challenges for large language models in multilingual code generation and translation.
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Specification-Driven Code Translation Powered by Large Language Models: How Far Are We?
NL specifications alone do not improve LLM code translation performance, but combining them with source code yields gains in select language pairs with no overall consistent benefit.
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A Survey on Large Language Models for Code Generation
A systematic literature review that organizes recent work on LLMs for code generation into a taxonomy covering data curation, model advances, evaluations, ethics, environmental impact, and applications, with benchmark comparisons.