arXiv (All)
open access
Characterizing Overconfident Failure in LLM-Based Code Generation
Large language models (LLMs) are increasingly used for automated code generation, but generated programs can appear syntactically plausible while still failing execution-based correctness checks. Existing validation methods, such as testing and program analysis, remain essential but are often incomp
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
- A Survey on Large Language Models for Code Generation#1135850
- Characterizing Overconfident Failure in LLM-Based Code Generation#1257961
- Detecting Inconsistencies in Model Specifications with LLM-as-Verifier Reasoning#1169612
- Detecting Inconsistencies in Model Specifications with LLM-as-Verifier Reasoning#1157118
- Beyond the Traceback: Using LLMs for Adaptive Explanations of Programming Errors#662088
Record · ID 1248580
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