LLM adaptive exploration via runtime code execution outperforms static query generation for information extraction from heterogeneous BIM models on the new ifc-bench v2 benchmark.
When llms meet api documentation: Can retrieval augmentation aid code generation just as it helps developers?
4 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Cross-lingual RACG shows non-trivial but unequal knowledge transfer across 13 programming languages, depending on linguistic affinity and pretraining diversity, with limited reliance on natural language information when using code-specific retrievers.
Replication finds Java security API misuse persists in current LLMs but is reduced by external knowledge in a model-dependent manner.
A structured domain-knowledge translation guide injected into LLM prompts improves OS kernel specification generation from 55% to 97% Pass@1 across nine models.
citing papers explorer
-
BIM Information Extraction Through LLM-based Adaptive Exploration
LLM adaptive exploration via runtime code execution outperforms static query generation for information extraction from heterogeneous BIM models on the new ifc-bench v2 benchmark.
-
Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation
Cross-lingual RACG shows non-trivial but unequal knowledge transfer across 13 programming languages, depending on linguistic affinity and pretraining diversity, with limited reliance on natural language information when using code-specific retrievers.
-
R+R: Reassessing Java Security API Misuse in Current LLMs: A Replication on JCA and JSSE APIs with External Security Knowledge
Replication finds Java security API misuse persists in current LLMs but is reduced by external knowledge in a model-dependent manner.
-
BODHI: Precise OS Kernel Specification Inference
A structured domain-knowledge translation guide injected into LLM prompts improves OS kernel specification generation from 55% to 97% Pass@1 across nine models.