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pro vyhledávání: '"von Rütte, Dimitri"'
Concept guidance has emerged as a cheap and simple way to control the behavior of language models by probing their hidden representations for concept vectors and using them to perturb activations at inference time. While the focus of previous work ha
Externí odkaz:
http://arxiv.org/abs/2402.14433
In an era where visual content generation is increasingly driven by machine learning, the integration of human feedback into generative models presents significant opportunities for enhancing user experience and output quality. This study explores st
Externí odkaz:
http://arxiv.org/abs/2307.10159
Autor:
Köpf, Andreas, Kilcher, Yannic, von Rütte, Dimitri, Anagnostidis, Sotiris, Tam, Zhi-Rui, Stevens, Keith, Barhoum, Abdullah, Duc, Nguyen Minh, Stanley, Oliver, Nagyfi, Richárd, ES, Shahul, Suri, Sameer, Glushkov, David, Dantuluri, Arnav, Maguire, Andrew, Schuhmann, Christoph, Nguyen, Huu, Mattick, Alexander
Aligning large language models (LLMs) with human preferences has proven to drastically improve usability and has driven rapid adoption as demonstrated by ChatGPT. Alignment techniques such as supervised fine-tuning (SFT) and reinforcement learning fr
Externí odkaz:
http://arxiv.org/abs/2304.07327
Generating music with deep neural networks has been an area of active research in recent years. While the quality of generated samples has been steadily increasing, most methods are only able to exert minimal control over the generated sequence, if a
Externí odkaz:
http://arxiv.org/abs/2201.10936