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pro vyhledávání: '"Leidinger, Alina"'
Autor:
Leidinger, Alina, Rogers, Richard
With the widespread availability of LLMs since the release of ChatGPT and increased public scrutiny, commercial model development appears to have focused their efforts on 'safety' training concerning legal liabilities at the expense of social impact
Externí odkaz:
http://arxiv.org/abs/2407.11733
Recent scholarship on reasoning in LLMs has supplied evidence of impressive performance and flexible adaptation to machine generated or human feedback. Nonmonotonic reasoning, crucial to human cognition for navigating the real world, remains a challe
Externí odkaz:
http://arxiv.org/abs/2406.06590
Autor:
Pistilli, Giada, Leidinger, Alina, Jernite, Yacine, Kasirzadeh, Atoosa, Luccioni, Alexandra Sasha, Mitchell, Margaret
This paper introduces the "CIVICS: Culturally-Informed & Values-Inclusive Corpus for Societal impacts" dataset, designed to evaluate the social and cultural variation of Large Language Models (LLMs) across multiple languages and value-sensitive topic
Externí odkaz:
http://arxiv.org/abs/2405.13974
The latest generation of LLMs can be prompted to achieve impressive zero-shot or few-shot performance in many NLP tasks. However, since performance is highly sensitive to the choice of prompts, considerable effort has been devoted to crowd-sourcing p
Externí odkaz:
http://arxiv.org/abs/2311.01967
Autor:
Starace, Giulio, Papakostas, Konstantinos, Choenni, Rochelle, Panagiotopoulos, Apostolos, Rosati, Matteo, Leidinger, Alina, Shutova, Ekaterina
Large Language Models (LLMs) exhibit impressive performance on a range of NLP tasks, due to the general-purpose linguistic knowledge acquired during pretraining. Existing model interpretability research (Tenney et al., 2019) suggests that a linguisti
Externí odkaz:
http://arxiv.org/abs/2310.18696
Autor:
Solaiman, Irene, Talat, Zeerak, Agnew, William, Ahmad, Lama, Baker, Dylan, Blodgett, Su Lin, Chen, Canyu, Daumé III, Hal, Dodge, Jesse, Duan, Isabella, Evans, Ellie, Friedrich, Felix, Ghosh, Avijit, Gohar, Usman, Hooker, Sara, Jernite, Yacine, Kalluri, Ria, Lusoli, Alberto, Leidinger, Alina, Lin, Michelle, Lin, Xiuzhu, Luccioni, Sasha, Mickel, Jennifer, Mitchell, Margaret, Newman, Jessica, Ovalle, Anaelia, Png, Marie-Therese, Singh, Shubham, Strait, Andrew, Struppek, Lukas, Subramonian, Arjun
Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of evaluating those impacts or for which impacts should be evaluated. In this
Externí odkaz:
http://arxiv.org/abs/2306.05949
Autor:
van der Wal, Oskar, Bachmann, Dominik, Leidinger, Alina, van Maanen, Leendert, Zuidema, Willem, Schulz, Katrin
Publikováno v:
Journal of Artificial Intelligence Research, 79, 1-40 (2024)
As Large Language Models and Natural Language Processing (NLP) technology rapidly develop and spread into daily life, it becomes crucial to anticipate how their use could harm people. One problem that has received a lot of attention in recent years i
Externí odkaz:
http://arxiv.org/abs/2211.13709
Autor:
van der Wal, Oskar, Bachmann, Dominik, Leidinger, Alina, van Maanen, Leendert, Zuidema, Willem, Schulz, Katrin
Publikováno v:
Journal of Artificial Intelligence Research; 2024, Vol. 79, p1-40, 40p
Akademický článek
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Autor:
van der Wal, Oskar, Bachmann, Dominik, Leidinger, Alina, van Maanen, Leendert, Zuidema, Willem, Schulz, Katrin
As Large Language Models and Natural Language Processing (NLP) technology rapidly develops and spreads into daily life, it becomes crucial to anticipate how its use could harm people. One problem that has received a lot of attention in recent years i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c33bb1b7a215d0994f4e20d8d9a48677
http://arxiv.org/abs/2211.13709
http://arxiv.org/abs/2211.13709