Výsledky vyhledávání - "Tanwar, Eshaan"

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Language Models can Exploit Cross-Task In-context Learning for Data-Scarce Novel Tasks

Autor: Chatterjee, Anwoy, Tanwar, Eshaan, Dutta, Subhabrata, Chakraborty, Tanmoy

Large Language Models (LLMs) have transformed NLP with their remarkable In-context Learning (ICL) capabilities. Automated assistants based on LLMs are gaining popularity; however, adapting them to novel tasks is still challenging. While colossal mode

Externí odkaz: http://arxiv.org/abs/2405.10548

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Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment

Autor: Tanwar, Eshaan, Dutta, Subhabrata, Borthakur, Manish, Chakraborty, Tanmoy

In-context learning (ICL) unfolds as large language models become capable of inferring test labels conditioned on a few labeled samples without any gradient update. ICL-enabled large language models provide a promising step forward toward bypassing r

Externí odkaz: http://arxiv.org/abs/2305.05940

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Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection

Autor: Awal, Md Rabiul, Lee, Roy Ka-Wei, Tanwar, Eshaan, Garg, Tanmay, Chakraborty, Tanmoy

Hate speech in social media is a growing phenomenon, and detecting such toxic content has recently gained significant traction in the research community. Existing studies have explored fine-tuning language models (LMs) to perform hate speech detectio

Externí odkaz: http://arxiv.org/abs/2303.02513

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