Few-shot prompting improves syntactic validity of LLM-generated code across ATL, ETL, QVTo, and Reactions, but semantic correctness gains remain uneven and language-dependent.
Model-Based Trust Analysis of LLM Conversations , year =
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EMFular generates ready-to-use Angular-based graphical EMF model editors from Ecore metamodels that run entirely in the browser while preserving EMF consistency and interoperability.
LLMs achieve Pearson correlations up to 0.97 and 94% classification accuracy on product desirability sentiment from qualitative data, outperforming lexicon and transformer baselines while providing confidence ratings and rationales.
citing papers explorer
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LLM4MTLs: Automated Generation and Empirical Evaluation of Model Transformation Languages
Few-shot prompting improves syntactic validity of LLM-generated code across ATL, ETL, QVTo, and Reactions, but semantic correctness gains remain uneven and language-dependent.
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Web-Native Graphical EMF Model Editors
EMFular generates ready-to-use Angular-based graphical EMF model editors from Ecore metamodels that run entirely in the browser while preserving EMF consistency and interoperability.
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Evaluating LLM Usage for Efficient and Explainable Numerical and Classified Implicit Sentiment Analysis of Product Desirability
LLMs achieve Pearson correlations up to 0.97 and 94% classification accuracy on product desirability sentiment from qualitative data, outperforming lexicon and transformer baselines while providing confidence ratings and rationales.