Personalized Abstractive Summarization by Tri-agent Generation Pipeline
Conference of the European Chapter of the Association for Computational Linguistics(2023)
摘要
Tailoring outputs from large language models, like ChatGPT, to implicit user
preferences remains a challenge despite their impressive generative
capabilities. In this paper, we propose a tri-agent generation pipeline
comprising a generator, an instructor, and an editor to enhance output
personalization. The generator produces an initial output, the instructor
automatically generates editing instructions based on user preferences, and the
editor refines the output to align with those preferences. The inference-only
large language model (ChatGPT) serves as both the generator and editor, with a
smaller model acting as the instructor to guide output generation. We train the
instructor using editor-steered reinforcement learning, leveraging feedback
from a large-scale editor model to optimize instruction generation.
Experimental results on two abstractive summarization datasets demonstrate the
effectiveness of our approach in generating outputs that better meet user
expectations. Code is available at
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