InheritSumm: A General, Versatile and Compact Summarizer by Distilling
from GPT
- URL: http://arxiv.org/abs/2305.13083v1
- Date: Mon, 22 May 2023 14:52:32 GMT
- Title: InheritSumm: A General, Versatile and Compact Summarizer by Distilling
from GPT
- Authors: Yichong Xu, Ruochen Xu, Dan Iter, Yang Liu, Shuohang Wang, Chenguang
Zhu, Michael Zeng
- Abstract summary: InheritSumm is a versatile and compact summarization model derived from GPT-3.5 through distillation.
It achieves similar or superior performance to GPT-3.5 in zeroshot and fewshot settings.
- Score: 75.29359361404073
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: While large models such as GPT-3 demonstrate exceptional performance in
zeroshot and fewshot summarization tasks, their extensive serving and
fine-tuning costs hinder their utilization in various applications. Conversely,
previous studies have found that although automatic metrics tend to favor
smaller fine-tuned models, the quality of the summaries they generate is
inferior to that of larger models like GPT-3 when assessed by human evaluators.
To address this issue, we propose InheritSumm, a versatile and compact
summarization model derived from GPT-3.5 through distillation. InheritSumm not
only exhibits comparable zeroshot and fewshot summarization capabilities to
GPT-3.5 but is also sufficiently compact for fine-tuning purposes. Experimental
results demonstrate that InheritSumm achieves similar or superior performance
to GPT-3.5 in zeroshot and fewshot settings. Furthermore, it outperforms the
previously established best small models in both prefix-tuning and full-data
fine-tuning scenarios.
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