@inproceedings{033958ddfe044a20b1076865d0ca3d57,
title = "AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization",
abstract = "Despite the advances in the abstractive summarization task using Large Language Models (LLM), there is a lack of research that assess their abilities to easily adapt to different domains. We evaluate the domain adaptation abilities of a wide range of LLMs on the summarization task across various domains in both fine-tuning and in-context learning settings. We also present AdaptEval, the first domain adaptation evaluation suite. AdaptEval includes a domain benchmark and a set of metrics to facilitate the analysis of domain adaptation. Our results demonstrate that LLMs exhibit comparable performance in the in-context learning setting, regardless of their parameter scale.",
author = "Anum Afzal and Ribin Chalumattu and Florian Matthes and Laura Mascarell",
note = "Publisher Copyright: {\textcopyright}2024 Association for Computational Linguistics.; 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual, CustomNLP4U 2024 ; Conference date: 16-11-2024",
year = "2024",
language = "English",
series = "1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual, CustomNLP4U 2024 - Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "76--85",
editor = "Sachin Kumar and Vidhisha Balachandran and Park, {Chan Young} and Weijia Shi and Hayati, {Shirley Anugrah} and Yulia Tsvetkov and Smith, {Noah A.} and Hannaneh Hajishirzi and Dongyeop Kang and David Jurgens",
booktitle = "1st Workshop on Customizable NLP",
}