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pro vyhledávání: '"Ungless, Eddie L."'
Autor:
Ungless, Eddie L., Vitsakis, Nikolas, Talat, Zeerak, Garforth, James, Ross, Björn, Onken, Arno, Kasirzadeh, Atoosa, Birch, Alexandra
This whitepaper offers an overview of the ethical considerations surrounding research into or with large language models (LLMs). As LLMs become more integrated into widely used applications, their societal impact increases, bringing important ethical
Externí odkaz:
http://arxiv.org/abs/2410.19812
TikTok has seen exponential growth as a platform, fuelled by the success of its proprietary recommender algorithm which serves tailored content to every user - though not without controversy. Users complain of their content being unfairly suppressed
Externí odkaz:
http://arxiv.org/abs/2407.14164
Autor:
Sigurgeirsson, Atli, Ungless, Eddie L.
Modern voice cloning models claim to be able to capture a diverse range of voices. We test the ability of a typical pipeline to capture the style known colloquially as "gay voice" and notice a homogenisation effect: synthesised speech is rated as sou
Externí odkaz:
http://arxiv.org/abs/2406.07504
This paper investigates the direct risks and harms associated with modern text-to-image generative models, such as DALL-E and Midjourney, through a comprehensive literature review. While these models offer unprecedented capabilities for generating im
Externí odkaz:
http://arxiv.org/abs/2307.05543
Cutting-edge image generation has been praised for producing high-quality images, suggesting a ubiquitous future in a variety of applications. However, initial studies have pointed to the potential for harm due to predictive bias, reflecting and pote
Externí odkaz:
http://arxiv.org/abs/2305.17072
The Stereotype Content model (SCM) states that we tend to perceive minority groups as cold, incompetent or both. In this paper we adapt existing work to demonstrate that the Stereotype Content model holds for contextualised word embeddings, then use
Externí odkaz:
http://arxiv.org/abs/2210.14552
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