AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization

• URL: http://arxiv.org/abs/2407.11591v3
• Date: Fri, 11 Oct 2024 09:23:43 GMT
• Title: AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization
• Authors: Anum Afzal, Ribin Chalumattu, Florian Matthes, Laura Mascarell,
• Abstract summary: We evaluate the domain adaptation abilities of Large Language Models (LLM) on the summarization task across various domains. We present AdaptEval, the first domain adaptation evaluation suite.
• Score: 4.07484910093752
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the advances in the abstractive summarization task using Large Language Models (LLM), there is a lack of research that asses 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.

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