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pro vyhledávání: '"Jones, Cameron A."'
Autor:
Jones, Cameron R., Bergen, Benjamin K.
Large Language Models (LLMs) can generate content that is as persuasive as human-written text and appear capable of selectively producing deceptive outputs. These capabilities raise concerns about potential misuse and unintended consequences as these
Externí odkaz:
http://arxiv.org/abs/2412.17128
Autor:
Steinacker, Paul, Tanttu, Tuomo, Lim, Wee Han, Stuyck, Nard Dumoulin, Feng, MengKe, Serrano, Santiago, Vahapoglu, Ensar, Su, Rocky Y., Huang, Jonathan Y., Jones, Cameron, Itoh, Kohei M., Hudson, Fay E., Escott, Christopher C., Morello, Andrea, Saraiva, Andre, Yang, Chih Hwan, Dzurak, Andrew S., Laucht, Arne
Superior computational power promised by quantum computers utilises the fundamental quantum mechanical principle of entanglement. However, achieving entanglement and verifying that the generated state does not follow the principle of local causality
Externí odkaz:
http://arxiv.org/abs/2407.15778
Everyday AI detection requires differentiating between people and AI in informal, online conversations. In many cases, people will not interact directly with AI systems but instead read conversations between AI systems and other people. We measured h
Externí odkaz:
http://arxiv.org/abs/2407.08853
Recent empirical results have sparked a debate about whether or not Large Language Models (LLMs) are capable of Theory of Mind (ToM). While some have found LLMs to be successful on ToM evaluations such as the False Belief task (Kosinski, 2023), other
Externí odkaz:
http://arxiv.org/abs/2406.14737
Autor:
Jones, Cameron R., Bergen, Benjamin K.
We evaluated 3 systems (ELIZA, GPT-3.5 and GPT-4) in a randomized, controlled, and preregistered Turing test. Human participants had a 5 minute conversation with either a human or an AI, and judged whether or not they thought their interlocutor was h
Externí odkaz:
http://arxiv.org/abs/2405.08007
Autor:
Jones, Cameron R., Bergen, Benjamin K.
We evaluated GPT-4 in a public online Turing test. The best-performing GPT-4 prompt passed in 49.7% of games, outperforming ELIZA (22%) and GPT-3.5 (20%), but falling short of the baseline set by human participants (66%). Participants' decisions were
Externí odkaz:
http://arxiv.org/abs/2310.20216
Autor:
Jones, Cameron R.1 (AUTHOR) cameron@ucsd.edu, Bergen, Benjamin1 (AUTHOR) bkbergen@ucsd.edu, Trott, Sean1 (AUTHOR) sttrott@ucsd.edu
Publikováno v:
Computational Linguistics. Dec2024, Vol. 50 Issue 4, p1415-1440. 26p.
Humans can attribute beliefs to others. However, it is unknown to what extent this ability results from an innate biological endowment or from experience accrued through child development, particularly exposure to language describing others' mental s
Externí odkaz:
http://arxiv.org/abs/2209.01515
Publikováno v:
In Journal of Organometallic Chemistry 15 May 2024 1012
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