Summary of From Tarzan to Tolkien: Controlling the Language Proficiency Level Of Llms For Content Generation, by Ali Malik et al.
From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generation
by Ali Malik, Stephen Mayhew, Chris Piech, Klinton Bicknell
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel framework proposed in this paper tackles the challenge of controlling the difficulty level of text generated by Large Language Models (LLMs) for contexts where end-users may not be proficient, such as language learners. The study evaluates the effectiveness of several key approaches for this task, including few-shot prompting, supervised finetuning, and reinforcement learning (RL), utilizing both GPT-4 and open-source alternatives like LLama2-7B and Mistral-7B. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make text generated by language models more suitable for people who don’t speak the language fluently. It tests different ways to control the difficulty level of this text, including using prompts, fine-tuning models, and training them through trial and error. The study uses two types of large language models: GPT-4, which is a well-known model, and three open-source alternatives. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Gpt » Prompting » Reinforcement learning » Supervised
