We study the problem of controlling the difficulty level of text generated by Large Language Models (LLMs) for contexts where end-users are not fully proficient, such as language learners. Using a novel framework, we evaluate the effectiveness of several key approaches for this task, including few-shot prompting, supervised finetuning, and reinforcement learning (RL), utilising both GPT-4 and open source alternatives like LLama2-7B and Mistral-7B. Our findings reveal a large performance gap between GPT-4 and the open source models when using prompt-based strategies. However, we show how to bridge this gap with a careful combination of finetuning and RL alignment. Our best model, CALM (CEFR-Aligned Language Model), surpasses the performance of GPT-4 and other strategies, at only a fraction of the cost. We further validate the quality of our results through a small-scale human study.
@article{arxiv.2406.03030,
title = {From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation},
author = {Ali Malik and Stephen Mayhew and Chris Piech and Klinton Bicknell},
journal= {arXiv preprint arXiv:2406.03030},
year = {2024}
}