Ambig-IaC detects structural disagreements in LLM-generated IaC candidates across three hierarchical axes to produce clarification questions, improving structure and attribute accuracy by 18.4% and 25.4% on a new 300-task benchmark.
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A distribution-free abstention rule grounded in multiple hypothesis testing uses execution consistency to let code LLMs avoid hallucination-prone tasks with theoretical guarantees.
Babbling Suppression stops LLM code generation upon test passage to reduce token output and energy consumption by up to 65% across Python and Java benchmarks.
citing papers explorer
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Ambig-IaC: Multi-level Disambiguation for Interactive Cloud Infrastructure-as-Code Synthesis
Ambig-IaC detects structural disagreements in LLM-generated IaC candidates across three hierarchical axes to produce clarification questions, improving structure and attribute accuracy by 18.4% and 25.4% on a new 300-task benchmark.
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Task Abstention for Large Language Models in Code Generation
A distribution-free abstention rule grounded in multiple hypothesis testing uses execution consistency to let code LLMs avoid hallucination-prone tasks with theoretical guarantees.
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Babbling Suppression: Making LLMs Greener One Token at a Time
Babbling Suppression stops LLM code generation upon test passage to reduce token output and energy consumption by up to 65% across Python and Java benchmarks.