AdaptEval: Evaluating Large Language Models on Domain Adaptation for Text Summarization
It addresses the lack of research on domain adaptation in summarization for LLM users, but is incremental as it introduces a new evaluation suite.
The paper tackles the problem of evaluating large language models' ability to adapt to different domains for text summarization, finding that models show comparable performance in in-context learning regardless of parameter scale.
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.