ReadCtrl: Personalizing text generation with readability-controlled instruction learning
CoRR(2024)
摘要
Content generation conditioning on users's readability is an important
application for personalization. In an era of large language models (LLMs),
readability-controlled text generation based on LLMs has become increasingly
important. This paper introduces a novel methodology called
"Readability-Controlled Instruction Learning (ReadCtrl)," which aims to
instruction-tune LLMs to tailor users' readability levels. Unlike the
traditional methods, which primarily focused on categorical readability
adjustments typically classified as high, medium, and low or expert and
layperson levels with limited success, ReadCtrl introduces a dynamic framework
that enables LLMs to generate content at various (near continuous level)
complexity levels, thereby enhancing their versatility across different
applications. Our results show that the ReadCtrl-Mistral-7B models
significantly outperformed strong baseline models such as GPT-4 and Claude-3,
with a win rate of 52.1
Read-Ctrl has shown significant improvements in automatic evaluations, as
evidenced by better readability metrics (e.g., FOG, FKGL) and generation
quality metrics (e.g., BLEU, SARI, SummaC-Factuality, UniEval-Consistency and
Coherence). These results underscore Read-Ctrl's effectiveness and tenacity in
producing high-quality, contextually appropriate outputs that closely align
with targeted readability levels, marking a significant advancement in
personalized content generation using LLMs.
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