@inproceedings{zhu-etal-2024-explanation,
title = "Explanation in the Era of Large Language Models",
author = "Zhu, Zining and
Chen, Hanjie and
Ye, Xi and
Lyu, Qing and
Tan, Chenhao and
Marasovic, Ana and
Wiegreffe, Sarah",
editor = "Zhang, Rui and
Schneider, Nathan and
Chaturvedi, Snigdha",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-tutorials.3",
pages = "19--25",
abstract = "Explanation has long been a part of communications, where humans use language to elucidate each other and transmit information about the mechanisms of events. There have been numerous works that study the structures of the explanations and their utility to humans. At the same time, explanation relates to a collection of research directions in natural language processing (and more broadly, computer vision and machine learning) where researchers develop computational approaches to explain the (usually deep neural network) models. Explanation has received rising attention. In recent months, the advance of large language models (LLMs) provides unprecedented opportunities to leverage their reasoning abilities, both as tools to produce explanations and as the subjects of explanation analysis. On the other hand, the sheer sizes and the opaque nature of LLMs introduce challenges to the explanation methods. In this tutorial, we intend to review these opportunities and challenges of explanations in the era of LLMs, connect lines of research previously studied by different research groups, and hopefully spark thoughts of new research directions",
}
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%0 Conference Proceedings
%T Explanation in the Era of Large Language Models
%A Zhu, Zining
%A Chen, Hanjie
%A Ye, Xi
%A Lyu, Qing
%A Tan, Chenhao
%A Marasovic, Ana
%A Wiegreffe, Sarah
%Y Zhang, Rui
%Y Schneider, Nathan
%Y Chaturvedi, Snigdha
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F zhu-etal-2024-explanation
%X Explanation has long been a part of communications, where humans use language to elucidate each other and transmit information about the mechanisms of events. There have been numerous works that study the structures of the explanations and their utility to humans. At the same time, explanation relates to a collection of research directions in natural language processing (and more broadly, computer vision and machine learning) where researchers develop computational approaches to explain the (usually deep neural network) models. Explanation has received rising attention. In recent months, the advance of large language models (LLMs) provides unprecedented opportunities to leverage their reasoning abilities, both as tools to produce explanations and as the subjects of explanation analysis. On the other hand, the sheer sizes and the opaque nature of LLMs introduce challenges to the explanation methods. In this tutorial, we intend to review these opportunities and challenges of explanations in the era of LLMs, connect lines of research previously studied by different research groups, and hopefully spark thoughts of new research directions
%U https://aclanthology.org/2024.naacl-tutorials.3
%P 19-25
Markdown (Informal)
[Explanation in the Era of Large Language Models](https://aclanthology.org/2024.naacl-tutorials.3) (Zhu et al., NAACL 2024)
ACL
- Zining Zhu, Hanjie Chen, Xi Ye, Qing Lyu, Chenhao Tan, Ana Marasovic, and Sarah Wiegreffe. 2024. Explanation in the Era 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 5: Tutorial Abstracts), pages 19–25, Mexico City, Mexico. Association for Computational Linguistics.