Readability reconsidered: A cross-dataset analysis of reference-free metric
An investigation of factors shaping human perceptions of text readability and comprehensibility.
Customizing language models' outputs to diverse readability levels is of paramount importance for effective communication. However, in highly regulated domains like finance, we need our outputs to be factually accurate as well. In this work, we conduct a cross-dataset empirical study of various readability metrics. Then, we propose a novel reinforcement learning based approach to training a model to adapt its response complexity to different user groups without sacrificing reasoning capabilities.
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