Summary of Readctrl: Personalizing Text Generation with Readability-controlled Instruction Learning, by Hieu Tran et al.
ReadCtrl: Personalizing text generation with readability-controlled instruction learning
by Hieu Tran, Zonghai Yao, Lingxi Li, Hong Yu
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces Readability-Controlled Instruction Learning (ReadCtrl), a novel methodology for instruction-tuning large language models (LLMs) to generate content at various complexity levels. Unlike traditional methods, ReadCtrl enables LLMs to produce text tailored to users’ readability needs, enhancing their versatility across different applications. The authors demonstrate the effectiveness of ReadCtrl by training models on the Mistral-7B dataset and comparing them to strong baseline models such as GPT-4 and Claude-3. The results show that ReadCtrl models significantly outperform baselines in both human evaluations (win rate of 52.1%:35.7% against GPT-4) and automatic readability and generation quality metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computers better at creating text that people can understand. They want to teach computers to write in a way that’s easy or hard, depending on who they’re writing for. This is important because computers are getting really good at writing stuff, but it’s not always the right level for everyone. The new method, called ReadCtrl, lets computers learn how to write in different ways and it works really well! They tested it against other strong computer models and showed that it can produce text that’s more readable and better quality than before. |
Keywords
» Artificial intelligence » Claude » Gpt » Instruction tuning
