From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation
<|reference_start|>From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation: 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.<|reference_end|>
@article{malik2024from,
    title={From Tarzan to Tolkien: Controlling the Language Proficiency Level of
  LLMs for Content Generation},
    author={Ali Malik, Stephen Mayhew, Chris Piech, Klinton Bicknell},
    journal={In Findings of the Association for Computational Linguistics (ACL
  2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03030},
    primaryClass={cs.CL cs.LG}
}