arXiv (All)
open access
Reducing Hallucinations in LLM-Generated Code via Semantic Triangulation
Large language models (LLMs) can generate executable code from natural language descriptions, but the resulting programs frequently contain bugs due to hallucinations. In the absence of formal specifications, existing approaches attempt to assess correctness using LLM-generated proxies such as tests
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Towards the Readability of LLM-Generated Codes through Multitask Representation Engineering#259536
- Execution-First Synthetic Tool-Use Trace Generation for LLM Agents#997524
- Compound Prompt Constraints in LLM Code Generation: A Factorial Study of Format, Persona, and Urgency#639746
- REAgent: Requirement-Driven LLM Agents for Software Issue Resolution#2697
- A Generalizable Framework for Building Executable Domain-Specific LLMs under Data Scarcity: Demonstration on Semiconductor TCAD Simulation#1013491
Record · ID 984927
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive