Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Ramesh, Krithika"'
Autor:
Ramesh, Krithika, Gandhi, Nupoor, Madaan, Pulkit, Bauer, Lisa, Peris, Charith, Field, Anjalie
The difficulty of anonymizing text data hinders the development and deployment of NLP in high-stakes domains that involve private data, such as healthcare and social services. Poorly anonymized sensitive data cannot be easily shared with annotators o
Externí odkaz:
http://arxiv.org/abs/2410.08327
Autor:
Ahuja, Kabir, Diddee, Harshita, Hada, Rishav, Ochieng, Millicent, Ramesh, Krithika, Jain, Prachi, Nambi, Akshay, Ganu, Tanuja, Segal, Sameer, Axmed, Maxamed, Bali, Kalika, Sitaram, Sunayana
Generative AI models have shown impressive performance on many Natural Language Processing tasks such as language understanding, reasoning, and language generation. An important question being asked by the AI community today is about the capabilities
Externí odkaz:
http://arxiv.org/abs/2303.12528
With language models becoming increasingly ubiquitous, it has become essential to address their inequitable treatment of diverse demographic groups and factors. Most research on evaluating and mitigating fairness harms has been concentrated on Englis
Externí odkaz:
http://arxiv.org/abs/2302.12578
Over the last few years, YouTube Kids has emerged as one of the highly competitive alternatives to television for children's entertainment. Consequently, YouTube Kids' content should receive an additional level of scrutiny to ensure children's safety
Externí odkaz:
http://arxiv.org/abs/2203.04837
In recent times, there has been definitive progress in the field of NLP, with its applications growing as the utility of our language models increases with advances in their performance. However, these models require a large amount of computational p
Externí odkaz:
http://arxiv.org/abs/2109.12584
Autor:
Naidu, Rakshit, Diddee, Harshita, Mulay, Ajinkya, Vardhan, Aleti, Ramesh, Krithika, Zamzam, Ahmed
In recent years, machine learning techniques utilizing large-scale datasets have achieved remarkable performance. Differential privacy, by means of adding noise, provides strong privacy guarantees for such learning algorithms. The cost of differentia
Externí odkaz:
http://arxiv.org/abs/2107.06946
With language models being deployed increasingly in the real world, it is essential to address the issue of the fairness of their outputs. The word embedding representations of these language models often implicitly draw unwanted associations that fo
Externí odkaz:
http://arxiv.org/abs/2106.08680
Publikováno v:
Competition Law International; Dec2019, Vol. 15 Issue 2, p191-197, 7p
