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.
Docprompting: Generating code by retrieving the docs
5 Pith papers cite this work, alongside 19 external citations. Polarity classification is still indexing.
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Co-locating tests with implementation code yields substantially higher preservation and correctness in foundation-model-generated programs than separated test syntax.
Retriever-side choices, particularly the retrieval algorithm, exert more influence on RAG performance than generator selection across code generation, summarization, and repair tasks.
ALGOGEN improves LLM-generated algorithm visualizations by splitting simulation into traceable JSON outputs via Visualization Trace Algebra and using Rendering Style Language for reliable rendering, raising success rates from 82.5% to 99.8% on 200 LeetCode tasks.
Empirical study identifies patterns in how model classes respond to structured prompts, optimization, and other techniques across two Verilog benchmarks.
citing papers explorer
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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.
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Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation
Co-locating tests with implementation code yields substantially higher preservation and correctness in foundation-model-generated programs than separated test syntax.
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Not All RAGs Are Created Equal: A Component-Wise Empirical Study for Software Engineering Tasks
Retriever-side choices, particularly the retrieval algorithm, exert more influence on RAG performance than generator selection across code generation, summarization, and repair tasks.
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ALGOGEN: Tool-Generated Verifiable Traces for Reliable Algorithm Visualization
ALGOGEN improves LLM-generated algorithm visualizations by splitting simulation into traceable JSON outputs via Visualization Trace Algebra and using Rendering Style Language for reliable rendering, raising success rates from 82.5% to 99.8% on 200 LeetCode tasks.
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VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation
Empirical study identifies patterns in how model classes respond to structured prompts, optimization, and other techniques across two Verilog benchmarks.