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Found 3 papers, 1 papers with code
Style Vectors for Steering Generative Large Language Model
This research explores strategies for steering the output of large language models (LLMs) towards specific styles, such as sentiment, emotion, or writing style, by adding style vectors to the activations of hidden layers during text generation.
Predicting Winning Regions in Parity Games via Graph Neural Networks (Extended Abstract)
We believe that this approach can be extended to efficiently solve parity games as well.
Topic Modelling of Empirical Text Corpora: Validity, Reliability, and Reproducibility in Comparison to Semantic Maps
Using the 6, 638 case descriptions of societal impact submitted for evaluation in the Research Excellence Framework (REF 2014), we replicate the topic model (Latent Dirichlet Allocation or LDA) made in this context and compare the results with factor-analytic results using a traditional word-document matrix (Principal Component Analysis or PCA).
