Summary of Closing the Loop: Learning to Generate Writing Feedback Via Language Model Simulated Student Revisions, by Inderjeet Nair et al.
Closing the Loop: Learning to Generate Writing Feedback via Language Model Simulated Student Revisions
by Inderjeet Nair, Jiaye Tan, Xiaotian Su, Anne Gere, Xu Wang, Lu Wang
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed paper, PROF (PROduces Feedback), aims to develop an automatic feedback generation system that optimizes student revising performance. Recent advances in language models have made it possible to generate actionable and attribute-aligned feedback, but the effectiveness of these models remains unclear. To address this challenge, the authors propose a novel approach that iteratively optimizes the feedback generator by maximizing students’ overall revising performance simulated by LMs. The proposed system, PROF, is tested on an economic essay assignment and outperforms various baseline methods in improving students’ writing quality, while also demonstrating enhanced pedagogical values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PROF is a new approach that helps students improve their writing skills by providing them with better feedback. The goal is to make the feedback as good as possible so that students can learn from it and get better at writing. To do this, PROF uses a special kind of computer program called a language model that simulates how students revise their work based on different types of feedback. The authors tested PROF on an economic essay assignment and found that it worked much better than other methods they tried. |
Keywords
* Artificial intelligence * Language model
