Empirical study across five LLMs and four languages finds security-aware prompting changes CWE category distributions but yields no statistically significant reduction in vulnerability frequency or density.
(2019) Language models are unsupervised multitask learners
2 Pith papers cite this work. Polarity classification is still indexing.
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RecPIE jointly optimizes recommendation predictions and LLM-generated natural-language explanations via alternating training and reinforcement learning, yielding 3-4% accuracy gains and higher human preference on Google Maps POI data.
citing papers explorer
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An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods
Empirical study across five LLMs and four languages finds security-aware prompting changes CWE category distributions but yields no statistically significant reduction in vulnerability frequency or density.
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Can Explanations Improve Recommendations? Evidence from Prediction-Informed Explanations
RecPIE jointly optimizes recommendation predictions and LLM-generated natural-language explanations via alternating training and reinforcement learning, yielding 3-4% accuracy gains and higher human preference on Google Maps POI data.