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3,629 papers
Large language models have significantly enriched the toolkit for computational humor research, particularly in the automated generation of jokes and puns. A key innovation, contextual embedding vectors, offers new opportunities to revisit and refine earlier hypotheses. Notably, Petrovic and Matthews (2013) proposed a...
This work studies six functionally distinct web applications, each generated in two prompt variants that are identical except for an appended security-requirements section: a baseline (A) and a security-aware (B) variant.
The results suggest that dialogue format should be selected according to learner characteristics in TTS dialogue-based lessons, with a significant interaction between learner characteristics and dialogue format for ARCS-based motivation.
The findings show that LLMs can approximate structured cybersecurity reasoning under controlled representations, but do not apply it robustly, which has important implications for the design and evaluation of AI-assisted security decision-support systems.
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This work establishes a minimax lower bound under the score-entropy loss, and proposes an MLE-based thresholding estimator that matches this lower bound up to constant and polylogarithmic factors that depend on neighboring density ratios.
SmolDocling, a compact 256M-parameter vision-language model (VLM), is fine-tune to perform end-to-end key-value extraction directly from document images, jointly solving identification, localization, and association in a single pass without OCR preprocessing.
GRASSP provides a competitive framework for integrating pretrained RNA representations with spatial structural context while reducing reliance on additional handcrafted structural annotations, and is demonstrated to outperform state-of-the-art baselines.
The feasibility of yoga nidra as a complementary intervention for autistic children is supported and directions for future research are suggested, including larger trials and further co-design with the autistic community.
Contemporary language models are dominated by the transformer architecture, which leverages self-attention mechanisms to enable more efficient, parallelized training across a wide set of documents and corpora. This has allowed transformers to effectively model data across a wide range of modalities and contexts. Howeve...
These findings show that reliable evaluation of LLM-generated code requires validated ground truth, protected tests, and multiple explicitly interpreted measures, and that CodeAssay provides a reproducible basis for evidence-based model evaluation in AI-augmented software development.
Programmable Logic Controllers (PLCs) are central to industrial automation, yet their programming remains largely manual and time-consuming. While Large Language Models (LLMs) excel at general-purpose code generation, their use in industrial control is limited by tight coupling with physical hardware, constrained I/O s...
The first comprehensive benchmarking framework specifically designed to accommodate inter-dataset heterogeneity is presented, finding that well-designed small datasets can match or even surpass the performance of larger benchmarks, suggesting that different metrics are applicable to different datasets/testing scenarios...
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