@inproceedings{ye-2024-cross,
title = "Cross-Task Generalization Abilities of Large Language Models",
author = "Ye, Qinyuan",
editor = "Cao, Yang (Trista) and
Papadimitriou, Isabel and
Ovalle, Anaelia",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-srw.27",
pages = "255--262",
abstract = "Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge and experience obtained when learning prior tasks. Enabling similar cross-task generalization abilities in NLP systems is fundamental for approaching the goal of general intelligence and expanding the reach of language technology in the future.In this thesis proposal, I will present my work on (1) benchmarking cross-task generalization abilities with diverse NLP tasks; (2) developing model architectures for improving cross-task generalization abilities; (3) analyzing and predicting the generalization landscape of current state-of-the-art large language models. Additionally, I will outline future research directions, along with preliminary thoughts on addressing them.",
}
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%0 Conference Proceedings
%T Cross-Task Generalization Abilities of Large Language Models
%A Ye, Qinyuan
%Y Cao, Yang (Trista)
%Y Papadimitriou, Isabel
%Y Ovalle, Anaelia
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F ye-2024-cross
%X Humans can learn a new language task efficiently with only few examples, by leveraging their knowledge and experience obtained when learning prior tasks. Enabling similar cross-task generalization abilities in NLP systems is fundamental for approaching the goal of general intelligence and expanding the reach of language technology in the future.In this thesis proposal, I will present my work on (1) benchmarking cross-task generalization abilities with diverse NLP tasks; (2) developing model architectures for improving cross-task generalization abilities; (3) analyzing and predicting the generalization landscape of current state-of-the-art large language models. Additionally, I will outline future research directions, along with preliminary thoughts on addressing them.
%U https://aclanthology.org/2024.naacl-srw.27
%P 255-262
Markdown (Informal)
[Cross-Task Generalization Abilities of Large Language Models](https://aclanthology.org/2024.naacl-srw.27) (Ye, NAACL 2024)
ACL
- Qinyuan Ye. 2024. Cross-Task Generalization Abilities of Large Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), pages 255–262, Mexico City, Mexico. Association for Computational Linguistics.